Auxiliary and inductive integrated beam forming design method for active intelligent metasurface

Abstract

The invention provides a beam forming design method for active intelligent metasurface auxiliary and inductance integration. The beam forming design method comprises the following steps: constructing an active intelligent metasurface auxiliary and inductance integration system model; the communication and sensing integrated system model comprises a co-located dual-function multi-antenna base station, an intelligent metasurface with transmitting and amplifying functions, a plurality of single-antenna communication users and a sensing target located in a detection blind area of the base station; a communication performance index is defined as a signal to interference plus noise ratio of a communication user, and a sensing performance index is defined as a Cramer-Rao lower bound of target direction arrival angle estimation; establishing a joint beamforming optimization problem, taking the minimum angle estimation Cramer-Rao bound as an optimization target, and simultaneously satisfying the conditions that the signal to interference plus noise ratio of a communication user is not less than a preset threshold value, the total transmitting power of a base station does not exceed a threshold value and the amplification power of the intelligent metasurface does not exceed a threshold value; the joint optimization problem is solved by using an alternate optimization algorithm. For the disclosed system, each variable is solved by adopting an iteration mode, and the method has the beneficial effects that the original problem is broken up into parts, and the problem solving complexity is greatly reduced while the system performance is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication and radar perception, and relates to a joint beamforming optimization method based on an active intelligent metasurface, which is used in a communication and perception integrated system to improve signal transmission performance and target perception accuracy. Background Art

[0002] The next generation of wireless networks requires both high-quality wireless connections and high-precision sensing capabilities to support smart manufacturing, smart transportation, smart healthcare, and other emerging applications. Traditional communication networks and radar systems are usually deployed independently, using different hardware platforms and spectrum resources. However, this independent deployment method leads to problems such as low spectrum utilization, high hardware costs, and increased system complexity. Therefore, Integrated Sensing and Communication (ISAC) technology, which can realize both communication and sensing functions, has become an important development direction for future wireless networks.

[0003] Thanks to the widespread application of millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies, communication signals show high resolution in the angular domain, which allows them to be used for high-precision target detection and parameter estimation. This feature makes ISAC technology a very promising solution. ISAC can realize communication and sensing functions simultaneously in the same spectrum by sharing a physical platform and dual-function transmission waveform. This technology not only significantly improves spectrum efficiency and energy efficiency, but also reduces hardware costs and signaling overhead, providing important support for applications in fields such as smart manufacturing, unmanned driving and the Internet of Things.

[0004] Reconfigurable intelligent surface (RIS) is recognized as one of the key technologies for the next generation of mobile communications because of its ability to intelligently reconstruct the wireless propagation environment. The intelligent metasurface is composed of a large number of passive electromagnetic components, each of which can independently adjust the phase shift of the incident signal. By intelligently coordinating the reflection characteristics of these components, RIS can establish a virtual line-of-sight (LoS) link between the transmitter and the receiver, thereby effectively alleviating channel fading and interference problems.

[0005] However, due to the existence of the "multiplicative fading effect", the performance gain of RIS is limited in many practical scenarios. The multiplicative fading effect refers to the equivalent path loss of the reflection link introduced by RIS, which is the product of the path loss of the transmitter-smart metasurface link and the smart metasurface-receiver link (rather than the sum of the path losses). This effect results in the performance gain provided by RIS being relatively limited when the direct link is strong or the receiver is far away from RIS.

[0006] In order to overcome the performance limitations of traditional RIS, active intelligent metasurface (Active RIS) has been proposed and gradually attracted attention. By integrating a reflective amplifier (such as a current converter or an integrated chip) into a traditional passive electromagnetic component, the active RIS can not only adjust the phase of the signal, but also amplify the signal. This design can effectively compensate for path loss, thereby alleviating the multiplicative fading effect and significantly improving the performance of communication and perception systems. The advantages of active RIS include that by amplifying the reflected signal, active RIS can effectively improve the received signal strength, improve the communication rate and perception accuracy; active RIS provides independent adjustment capabilities for phase and amplitude, bringing greater flexibility to beamforming and resource optimization; in the integrated communication and perception system, active RIS can amplify the weak radar echo signal twice, thereby improving the echo quality and enhancing the target detection and parameter estimation capabilities.

[0007] The receiver of the actual communication and perception integrated system is usually less sensitive than the traditional radar receiver due to hardware cost constraints. As a result, the receiver may not meet the accuracy requirements of target detection or parameter estimation when facing weak echo signals. In addition, since the active RIS integrates additional reflective amplifiers, its power limitation and thermal noise problems pose new challenges to the design. Therefore, how to design an efficient joint beamforming method to fully utilize the capabilities of the active RIS is an urgent problem to be solved in the ISAC system. Summary of the invention

[0008] Based on the above-mentioned technical problems of low radar echo signal quality and limited target perception range in the existing communication and perception integrated system, a beamforming design method for an active intelligent metasurface assisted communication and perception integrated system is provided.

[0009] The technical means adopted by the present invention are as follows:

[0010] A beamforming design method for active intelligent metasurface assisted synaesthesia integration, characterized by comprising the following steps:

[0011] Step 1: construct an active intelligent metasurface-assisted synaesthesia integrated system model; the synaesthesia integrated system model includes: a co-located dual-function multi-antenna base station, an intelligent metasurface with transmission and amplification functions, multiple single-antenna communication users, and a sensing target located in the base station detection blind spot;

[0012] Step 2: Define the communication performance index as the signal-to-interference-noise ratio of the communication user, and the perception performance index as the Cramer-Rao lower bound of the arrival angle estimation in the target direction;

[0013] Step 3: Establish a joint beamforming optimization problem, with the optimization goal of minimizing the angle estimation Cramer-Rao bound, while satisfying the conditions that the signal-to-interference-noise ratio of the communication user is not less than the preset threshold value, the total transmission power of the base station does not exceed the threshold, and the amplified power of the smart metasurface does not exceed the threshold;

[0014] Step 4: Use the alternating optimization algorithm to solve the joint optimization problem.

[0015] Furthermore, in the active intelligent metasurface-assisted integrated communication and perception system, the base station has N t Transmitting antennas and N r Root receiving antennas, with the assistance of active intelligent metasurface, communicate with K single-antenna users and sense a potential target; the direct link from the base station to the target is blocked by obstacles, that is, the target is located in the service blind spot of the base station; let the number of antennas N t =N r =N; the dual-function signal transmitted by the base station in the lth time slot is:

[0016] x[l]=W c s c [l]+W r s r [l] = Ws[l];

[0017] in, and Represent communication symbols and radar signals respectively, satisfying Matrix W c and W r They represent the communication / radar beamforming matrices respectively; in addition, the overall beamforming matrices are defined as The sent symbol vector is

[0018] Furthermore, the received signal of the kth communication user in the system model in the lth time slot is expressed as:

[0019]

[0020] in, represents the direct channel between the base station and the kth user, represents the channel between the base station and the active smart metasurface, represents the channel between the active smart metasurface and the kth user; represents the active smart metasurface reflection beamforming matrix, and is the reflection coefficient vector; the reflection coefficient φ of the active smart metasurface m Described as Amplitude a m In [0,a max ] Continuously adjustable; denote the noise at the active smart metasurface and at the kth user, respectively; therefore, the signal to interference noise ratio of the kth user is calculated as:

[0021]

[0022] in, represents the equivalent channel between the base station and the kth communication user, w i represents the i-th column of the transmit beamforming matrix W.

[0023] Furthermore, the radar echo signal received by the base station in the system model is expressed as:

[0024]

[0025] in, represents the radar cross section of the target, represents the LoS channel from the active smart metasurface to the target, α r,t represents the path loss, represents the steering vector associated with the arrival angle θ of the target relative to the active smart metasurface; the signal samples of L time slots are collected, and the combined received signal is expressed as:

[0026] Y r =αQWS+G T ΦZ1+N r ;

[0027] in, and are symbol / noise matrices respectively; the vectorized radar echo signal model is expressed as:

[0028]

[0029] Among them, the definition

[0030] Furthermore, define is the target parameter to be estimated, where Then the Cramer-Rao bound matrix of the target parameter estimate can be expressed as the inverse of the Fisher information matrix of the estimated parameter ξ; let Represents the estimated parameter ξFisher information matrix; based on the complex observation in Each element of the matrix M is calculated as:

[0031]

[0032] Split the matrix M into 2×2 block matrices:

[0033]

[0034] Each block is represented as:

[0035]

[0036] in, represents the partial derivative of the matrix Q with respect to θ, Therefore, the Cramer-Rao bound for estimating the DoAθ of the target is expressed as:

[0037]

[0038] Furthermore, the active intelligent metasurface-assisted integrated communication and perception system minimizes the Cramer-Rao bound of radar parameter estimation while ensuring the quality of service requirements of communication users by jointly designing the transmit beamforming W and the reflection beamforming of the active intelligent surface, and considers the following constraints; the constraints are:

[0039] (1) Transmit power limit: Where P BS represents the base station transmit power budget;

[0040] (2) Active intelligent metasurface reflection power limit: P(W,φ)≤P RIS , where P RIS represents the active smart metasurface reflection power budget, and P(W,φ) represents the active smart metasurface reflection power:

[0041]

[0042] (3) Communication user service quality restrictions: SINR k represents the signal-to-interference-to-noise ratio of the kth communication user, r k represents the service quality requirement of the kth user;

[0043] (4) Amplitude limit of active intelligent metasurface amplification factor: i.e. a m ≤a max, a m represents the amplitude of the reflection coefficient of the mth unit of the active smart metasurface, a max Indicates the maximum amplification factor that the active intelligent metasurface amplifier can provide;

[0044] Then, the objective equation is expressed as follows:

[0045]

[0046] Furthermore, the process of solving the objective equation is:

[0047] Step 1: Perform equivalent transformation on the target equation of the Cramer-Rao bound for target angle estimation;

[0048] Step 2: Under the condition of fixing the active intelligent metasurface reflection coefficient φ, optimize the base station transmit beamforming W: introduce auxiliary variable t into the objective function w The Schur's complement lemma is used to convert the design problem of base station transmit beamforming W into equivalent form. The semi-positive definite relaxation algorithm is used to solve the converted optimization problem and the optimized transmit beamforming matrix W is obtained.

[0049] Step 3: Under the condition of fixed base station transmit beamforming W, update the active smart metasurface reflection beamforming φ: introduce auxiliary variables Ψ and t into the objective function i , thereby transforming the fractional optimization problem of the active intelligent metasurface reflection coefficient φ into a polynomial problem, and further using the principal optimal minimization algorithm to transform the objective function into an easily solved convex function, and then iteratively updating each variable until convergence to obtain the reflection coefficient of the active intelligent metasurface.

[0050] Compared with the prior art, the present invention has the following advantages:

[0051] The present invention transforms the target equation in the first stage (S1), and converts the original problem into an equivalent form that is easy to solve. In the second stage (S2), the base station transmit beamforming is updated based on optimization methods such as auxiliary variables, Schur complement, and semi-positive relaxation algorithm. In the third stage (S3), the active intelligent metasurface reflection beamforming is iteratively updated based on optimization algorithms such as auxiliary variables, fractional programming, and primary optimal minimization. The present invention uses an iterative method to solve each variable for the disclosed system, and the beneficial effect brought about is that the original problem is broken down into parts, while ensuring system performance, the complexity of problem solving is greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 It is a schematic diagram of the active intelligent metasurface assisted communication and perception integrated system in the present invention;

[0054] Figure 2 A flowchart of a joint design algorithm for transmit beamforming and active intelligent metasurface reflective beamforming in an embodiment of the present invention;

[0055] Figure 3 It is a simulation diagram of the transmission power versus the Cramer-Rao bound in the present invention;

[0056] Figure 4 Simulation diagram comparing Cramer-Rao bounds with communication user signal-to-interference-to-noise ratio requirements;

[0057] Figure 5 A simulation diagram showing the number of elements on the smart metasurface compared to the Cramer-Rao bound. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0059] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0060] by Figure 1Take the active intelligent metasurface-assisted integrated communication and perception system shown as an example, in which a dual-function base station simultaneously performs multi-user communication and co-site radar perception functions.

[0061] Specifically,

[0062] The base station has N t Transmitting antennas and N r With the help of active intelligent metasurface, the base station has 4 receiving antennas, which communicate with K single-antenna users and sense a potential target. The direct link from the base station to the target is blocked by obstacles, that is, the target is located in the service blind spot of the base station. Assume that the number of antennas is N t =N r =N. The base station transmits a signal in the lth time slot:

[0063] x[l]=W c s c [l]+W r s r [l]=Ws[l] (2)

[0064] in and Represent communication symbols and radar signals respectively, satisfying Matrix W c and W r They represent the communication / radar beamforming matrices respectively; in addition, the overall beamforming matrices are defined as The sent symbol vector is

[0065] The received signal of the kth communication user in the lth time slot can be expressed as

[0066]

[0067] in represents the channel between the base station and the kth user, represents the channel between the base station and the active smart metasurface, represents the channel between the active smart metasurface and the kth user. represents the active smart metasurface reflection beamforming matrix, and is the reflection coefficient vector. The reflection coefficient φ of the active smart metasurface m Can be described as Amplitude a m In [0,a max ] is continuously adjustable. denote the noise at the active intelligent metasurface and at the kth user, respectively. The signal-to-interference-noise ratio of the kth user can be calculated as

[0068]

[0069] in is the equivalent channel between the base station and the kth communication user, w i Defined as the i-th column of the transmit beamforming matrix W.

[0070] Since the direct link from the base station to the target is blocked by obstacles, the transmitted signal can only reach the target through the reflection link assisted by the active intelligent metasurface and return via the same path. Therefore, the radar echo signal received by the base station can be expressed as

[0071]

[0072] in is the radar cross section of the target, is the LoS channel from the active smart metasurface to the target, α r,t represents the path loss,

[0073] represents the steering vector associated with the direct-of-arrival (DoA) θ of the target relative to the active smart metasurface. The signal samples of L time slots are collected, and the combined received signal can be expressed as

[0074] Y r =αQWS+G T φZ1+N r (6)

[0075] in is the equivalent channel of the target echo, is a symbolic matrix, and is the noise matrix. The vectorized signal model can be further expressed as

[0076]

[0077] where the definition

[0078] definition represents the target parameter to be estimated, and Since the Cramer-Rao bound matrix of the estimated parameter ξ is the inverse of its Fisher information matrix, we first calculate the Fisher information matrix of the estimated parameter ξ Based on the complex observation in Each element of the matrix M can be calculated as

[0079]

[0080] In order to solve the Cramer-Rao bound for target angle estimation, the matrix M needs to be split into 2×2 block matrices:

[0081]

[0082] Each sub-matrix can be expressed as

[0083]

[0084] It indicates represents the partial derivative of the matrix Q with respect to θ, The Cramer-Rao bound for estimating the DoAθ of a target can be expressed as:

[0085]

[0086] Based on the above active intelligent metasurface-assisted integrated communication and perception system, by jointly designing the transmit beamforming W and the reflection beamforming φ of the active intelligent surface, the Cramer-Rao bound of radar parameter estimation is minimized while ensuring the quality of service requirements of communication users, and the following constraints are considered:

[0087] (5) Transmit power limit: P BS Transmit power budget for base stations;

[0088] (6) Active intelligent metasurface reflection power limit: P(W,φ)≤P RIS , P RIS is the active smart metasurface reflection power budget, and P(W,φ) is the active smart metasurface reflection power:

[0089]

[0090] (7) Communication user service quality restrictions: SINR k represents the signal-to-interference-to-noise ratio of the kth communication user, r k is the service quality requirement of the kth user;

[0091] (8) Amplitude limit of active intelligent metasurface amplification factor: i.e. a m ≤a max , a m is the amplitude of the reflection coefficient of the mth unit of the active smart metasurface, a max This is the maximum amplification factor that the active intelligent metasurface amplifier can provide.

[0092] Therefore, the objective equation can be expressed as follows:

[0093]

[0094] Obviously, this optimization problem is non-convex, and it is difficult to solve directly due to the complex fractional form of the objective equation, the high-order terms of the optimization variables, and the coupling between different optimization variables. For the above problem, the present invention provides the following solution with a local optimal solution. Specifically, the block coordinate descent method is used to update each variable one by one, and the transmit beamforming and active intelligent metasurface reflection beamforming are iteratively solved. The flow chart is as follows: Figure 2 As shown, the steps are as follows:

[0095] S1: Transform the target equation and convert the original problem into a form that is easy to solve;

[0096] S2: Update base station transmit beamforming:

[0097] S2_01: Introducing auxiliary variable t w And by applying Schur complement, the design problem about W is further transformed into equivalent one;

[0098] S2_02: Use the semi-definite relaxation algorithm to solve the relevant optimization problems and construct the transmit beamforming;

[0099] S3: Update on Active Smart Metasurface Reflection Beamforming:

[0100] S3_01: Introduce auxiliary variable Ψ to simplify the complex optimization problem about φ;

[0101] S3_02: Introducing auxiliary variable t i , using fractional programming to further transform the problem;

[0102] S3_03: Apply the dominant minimization algorithm to iteratively solve the active smart metasurface reflection beamforming.

[0103] The above technical solution can effectively improve the performance of the integrated communication and perception system. In order to prove the feasibility of the above technical solution, the relevant simulation results are given. For the convenience of comparison, the present invention simulates the performance of two different systems as benchmarks: i) a passive intelligent metasurface-assisted integrated communication and perception system; ii) an active intelligent metasurface-assisted pure radar system. Figure 3 The relationship between the Cramer-Rao bound and the transmission power is shown. Figure 3 It can be seen that compared with the passive intelligent metasurface-assisted communication-perception integrated system, the active intelligent metasurface-assisted communication-perception integrated technical solution proposed in the present invention can always achieve better radar perception performance. Figure 4 The curve of the Cramer-Rao bound changing with the signal-to-interference-to-noise ratio requirement of the communication user is given. Figure 4 It can be seen that even if higher communication user service quality requirements are met, the technical solution proposed in the present invention can still achieve radar perception performance similar to that of a pure radar system. Figure 5The variation of the Cramer-Rao bound with the number of smart metasurface elements is given. As the number of smart metasurface elements increases, the technical solution proposed in the present invention can always maintain good Cramer-Rao bound performance.

[0104] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0105] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0107] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0108] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A beamforming design method for active intelligent metasurface assisted synaesthesia integration, characterized in that: The following steps are involved: Step 1: construct an active intelligent metasurface-assisted synaesthesia integrated system model; the synaesthesia integrated system model includes: a co-located dual-function multi-antenna base station, an intelligent metasurface with transmission and amplification functions, multiple single-antenna communication users, and a sensing target located in the base station detection blind spot; Step 2: Define the communication performance index as the signal-to-interference-noise ratio of the communication user, and the perception performance index as the Cramer-Rao lower bound of the arrival angle estimation in the target direction; Step 3: Establish a joint beamforming optimization problem, with the optimization goal of minimizing the angle estimation Cramer-Rao bound, while satisfying the conditions that the signal-to-interference-noise ratio of the communication user is not less than the preset threshold value, the total transmission power of the base station does not exceed the threshold, and the amplified power of the smart metasurface does not exceed the threshold; Step 4: Use the alternating optimization algorithm to solve the joint optimization problem.

2. The beamforming design method for active intelligent metasurface assisted synaesthesia integration according to claim 1 is characterized in that: In the active intelligent metasurface-assisted integrated communication and perception system, the base station has N t Transmitting antennas and N r Root receiving antennas, with the assistance of active intelligent metasurface, communicate with K single-antenna users and sense a potential target; the direct link from the base station to the target is blocked by obstacles, that is, the target is located in the service blind spot of the base station; let the number of antennas N t =N r =N; the dual-function signal transmitted by the base station in the lth time slot is: x[l]=W c s c [l]+W r s r [l]=Ws[l]; in, and Represent communication symbols and radar signals respectively, satisfying Matrix W c and W r They represent the communication / radar beamforming matrices respectively; in addition, the overall beamforming matrices are defined as The sent symbol vector is 3. The beamforming design method for active intelligent metasurface assisted synaesthesia integration according to claim 1 is characterized in that: The received signal of the kth communication user in the lth time slot in the system model is expressed as: in, represents the direct channel between the base station and the kth user, represents the channel between the base station and the active smart metasurface, represents the channel between the active smart metasurface and the kth user; represents the active smart metasurface reflection beamforming matrix, and is the reflection coefficient vector; the reflection coefficient φ of the active smart metasurface m Described as Amplitude a m In [0,a max ] Continuously adjustable; denote the noise at the active smart metasurface and at the kth user, respectively; therefore, the signal to interference noise ratio of the kth user is calculated as: in, represents the equivalent channel between the base station and the kth communication user, w i represents the i-th column of the transmit beamforming matrix W.

4. The beamforming design method for active intelligent metasurface assisted synaesthesia integration according to claim 1 is characterized in that: The radar echo signal received by the base station in the system model is expressed as: in, represents the radar cross section of the target, represents the LoS channel from the active smart metasurface to the target, α r,t represents the path loss, represents the steering vector associated with the arrival angle θ of the target relative to the active smart metasurface; the signal samples of L time slots are collected, and the combined received signal is expressed as: Y r =αQWS+G T ΦZ1+N r ; in, and are symbol / noise matrices respectively; the vectorized radar echo signal model is expressed as: Among them, the definition 5. The beamforming design method for active intelligent metasurface assisted synaesthesia integration according to claim 1 is characterized in that: definition is the target parameter to be estimated, where Then the Cramer-Rao bound matrix of the target parameter estimate can be expressed as the inverse of the Fisher information matrix of the estimated parameter ξ; let Represents the estimated parameter ξFisher information matrix; based on the complex observation in Each element of the matrix M is calculated as: Split the matrix M into 2×2 block matrices: Each block is represented as: in, represents the partial derivative of the matrix Q with respect to θ, Therefore, the Cramer-Rao bound for estimating the DoA θ of a target is expressed as:

6. The beamforming design method for active intelligent metasurface assisted synaesthesia integration according to claim 1 is characterized in that: The active intelligent metasurface-assisted integrated communication and perception system minimizes the Cramer-Rao bound of radar parameter estimation while ensuring the quality of service requirements of communication users by jointly designing the transmit beamforming W and the reflection beamforming of the active intelligent surface, and considers the following constraints; the constraints are: (1) Transmit power limit: Where P BS represents the base station transmit power budget; (2) Active intelligent metasurface reflection power limit: P(W,φ)≤P RIS , where P RIS represents the active smart metasurface reflection power budget, and P(W,φ) represents the active smart metasurface reflection power: (3) Communication user service quality restriction: SINR k ≥r k , SINR k represents the signal-to-interference-to-noise ratio of the kth communication user, r k represents the service quality requirement of the kth user; (4) Amplitude limit of active intelligent metasurface amplification factor: i.e. a m ≤a max , a m represents the amplitude of the reflection coefficient of the mth unit of the active smart metasurface, a max Indicates the maximum amplification factor that the active intelligent metasurface amplifier can provide; Then, the objective equation is expressed as follows:

7. The beamforming design method for active intelligent metasurface assisted synaesthesia integration according to claim 6 is characterized in that: The process of solving the objective equation is: Step 1: Perform equivalent transformation on the target equation of the Cramer-Rao bound for target angle estimation; Step 2: Under the condition of fixing the active intelligent metasurface reflection coefficient φ, optimize the base station transmit beamforming W: introduce auxiliary variable t into the objective function w And using the Schur's complement lemma, the design problem of the base station transmit beamforming W is equivalently transformed; The transformed optimization problem is solved using a semi-positive definite relaxation algorithm to obtain the optimized transmit beamforming matrix W; Step 3: Under the condition of fixed base station transmit beamforming W, update the active smart metasurface reflection beamforming φ: introduce auxiliary variables Ψ and t into the objective function i , thereby transforming the fractional optimization problem of the active intelligent metasurface reflection coefficient φ into a polynomial problem, and further using the principal optimal minimization algorithm to transform the objective function into an easily solved convex function, and then iteratively updating each variable until convergence to obtain the reflection coefficient of the active intelligent metasurface.

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