A communication perception integration method and equipment based on anti-clutter and intelligent reflective surface assistance

Abstract

The present invention discloses a communication perception integration method and equipment based on anti-clutter and intelligent reflective surface assistance, the method comprising: establishing a communication model, a radar perception model, and a clutter model; setting an active beam of a joint optimization base station, that is, a communication signal transmission beam {w k} and radar signal covariance matrix R0, set the passive beam of the intelligent reflective surface, where the reflection beam matrix of the intelligent reflective surface is Φ; at the maximum transmission power of the base station, ensure the minimum signal-to-noise ratio of two different communication receiver users to ensure the communication quality of the communication users, impose an upper limit of the tolerable clutter power on each clutter scatterer, and constrain the design of the cross-correlation pattern between radar signals to maximize the minimum beam gain of multiple sensing targets. The communication perception integration method and equipment based on anti-clutter and intelligent reflective surface assistance proposed by the present invention solves the technical problems of poor sensing and user communication performance of the communication perception integration technical system.

Description

Technical Field

[0001] The present invention relates to the technical field of communications, and in particular to a communication perception integration method and equipment based on anti-clutter and intelligent reflective surface assistance. Background Art

[0002] In the evolution of super fifth-generation and sixth-generation communication technologies, more and more sensing and communication services need to be completed simultaneously, such as autonomous driving, Internet of Vehicles, smart home and virtual reality. These functions require the simultaneous execution of sensing and communication services. However, with the increasing data traffic, high-frequency technology methods are urgently needed to maintain wireless devices with different service quality requirements, and the demand for ultra-low transmission delay and ultra-high communication reliability has emerged. Therefore, perception and communication need to be integrated as a pair of intertwined functions, which usually need to operate simultaneously. In fact, a single sensing or communication solution is not enough to meet the needs of the current wireless communication and sensing environment. Communication perception integration technology refers to the close integration of sensing and communication capabilities, which can provide sensing and communication services simultaneously with multiple sensing targets and communication users. The goal of communication perception integration technology is to integrate communication and radar sensing on the same platform and spectrum, which will improve resource efficiency and take advantage of each other's advantages. In addition, communication and radar sensing resources are shared in the communication and sensing integration technology, which improves the utilization efficiency in terms of spectrum, hardware, energy and cost. The communication and sensing integration technology integrates communication and sensing through the same platform and the same spectrum, in which wireless infrastructure and spectrum resources are reused for radar sensing, positioning and imaging, which can improve resource efficiency and achieve mutual benefit.

[0003] As a low-cost method to improve the performance of wireless network systems, smart reflective surfaces have attracted extensive research interest from academia and industry in recent years. Smart reflective surfaces are composed of a large number of reconfigurable passive components. When the signal collides with the smart reflective surface components, a certain phase shift will be generated, thereby synergistically changing the propagation of the reflected signal to adjust the signal propagation path. By properly designing the smart reflective surface controller and the smart reflective surface phase matrix, the desired signal and the desired signal power gain can be improved, a good communication channel can be established, and the system performance can be improved. It can also provide additional degrees of freedom to solve severe channel fading. Therefore, smart reflective surfaces are an effective way to handle communication services under poor channel conditions. Due to the excellent effect of smart reflective surfaces in wireless communication systems, in addition to studying auxiliary communications, smart reflective surfaces can also be introduced into radar and communication radar coexistence systems, and the communication perception integration technology has been studied. Smart reflective surfaces are able to configure wireless propagation environments to support wireless communications and target perception.

[0004] In a cluttered environment of an existing communication-sensing integrated technology system, the sensing link of the communication-sensing integrated technology system may be obstructed by clutter scatterers, which may result in poor sensing and user communication performance. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and to propose a communication perception integration method and equipment based on anti-clutter and intelligent reflective surface assistance, so as to solve the technical problem that the existing communication perception integration technology system may be hindered by clutter scatterers, which may lead to poor sensing and user communication performance.

[0006] To this end, the present invention discloses a communication perception integration method based on anti-clutter and intelligent reflective surface assistance, the method comprising: establishing a communication model, a radar perception model, and a clutter model; wherein the clutter model is expressed as:

[0007]

[0008] Define a new vector represents the effective link between the base station and the qth clutter, where and represent the clutter signal vectors between the base station and the smart reflector to the qth clutter, respectively, and w k represents the information beam vector of the communication user, and R0 represents the covariance matrix of the sensor signal;

[0009] Set the active beam of the joint optimization base station, that is, the communication signal transmission beam {w k} and radar signal covariance matrix R0, set the passive beam of the smart reflecting surface, where the reflection beam matrix Φ of the smart reflecting surface; at the maximum transmission power of the base station, ensure the minimum signal-to-noise ratio of two different communication receiver users to ensure the communication quality of the communication users, impose an upper bound of the tolerable clutter power on each clutter scatterer, and constrain the design of the cross-correlation pattern between radar signals. The optimization goal is to maximize the minimum beam gain of multiple sensing targets.

[0010] Preferably, the communication model is based on the model analysis of the downlink, and the communication user will receive two signals: a direct link from the base station and an indirect link reflected from the smart reflective surface; the smart reflective surface is equipped with N>1 reflective elements n∈{1, 2, ..., N}, and a related reflection beam matrix is ​​defined as represents the phase shift matrix of the N elements of the smart reflector, where φ n ∈(0, 2π], Each communication user is assigned a dedicated communication beam vector. The Kth user will receive signals from the base station and the smart reflector, so the received signal y at the Kth user is k It can be expressed as:

[0011]

[0012] Define a new vector represents the effective link between the base station and the Kth communication user; Represents the channel matrix of the link from the ground base station to the smart reflector. Represent the communication channel vectors from the ground base station and the smart reflector to the Kth communication user, respectively, [·] H Represents the conjugate transpose operation on the matrix. k ,s k They correspond to the communication signal beam vector at the kth user and the corresponding communication signal, x0 represents the radar sensor signal, represents the additive Gaussian white noise received at the kth user;

[0013] The first communication user is defined as type I, which has the ability to eliminate the influence of radar signals. For the kth communication user, user k will receive the desired signal beam w. k In addition, it will also receive the signal beam of other communication user j Therefore, the signal beams of other communication users will produce spatial interference suppression for the kth user; for type I communication users, the signal-to-noise ratio received at user k is It can be expressed as:

[0014]

[0015] where h CU，k represents the effective communication link between the base station and the communication user k, Represents the additive white Gaussian noise interference received by user k.

[0016] The second type of communication user is defined as type II. It does not have the ability to process radar signals, so the radar signal x0 will also affect the communication quality of the kth communication user; therefore, for type II communication users, the signal-to-noise ratio received at user k is It can be expressed as:

[0017]

[0018] where h CU，k represents the effective communication link between the base station and the communication user k, Represents the additive white Gaussian noise interference received by user k.

[0019] Preferably, sensing services are provided for potential L targets. The LoS link between the ground base station and the sensing target may be blocked by clutter objects. Therefore, a virtual LoS link created by the smart reflective surface is used to provide sensing services. In this case, the sensing target will be served by the smart reflective surface. If the sensing target is not blocked by clutter, the ground base station will also provide sensing services. Therefore, the sensing target will be jointly provided with sensing services by the base station and the smart reflective surface. The beam pattern gain at the potential target at the location of interest is used as the sensing performance metric. Let the set of potential sensing targets be And the angle of the sensing target relative to the smart reflective surface is {θ1,…,θ l}; The array steering vector at the smart reflective surface is

[0020]

[0021] Assume d IRS It represents the spacing between consecutive reflective elements at the smart reflective surface, and λ represents the wavelength.

[0022] And the communication signal s k and the radar signal x0 can jointly improve the sensing service for the sensing target, so the expected beam pattern gain at the lth sensing target is expressed as:

[0023]

[0024] One of the newly defined vectors represents the effective link between the base station and the lth sensor target, where represents the sensing channel vector from the ground base station to the lth sensing target, w k represents the information beam vector of the communication user, and R0 represents the covariance matrix of the sensor signal;

[0025] Consider the signal cross-correlation between two sensing targets, for the Ith sensor with an angle θ relative to the smart reflector l The sensing target and the i-th angle relative to the smart reflective surface is θ i The cross-correlation pattern between the sensing targets is expressed as P(θ l ,θ i ), which can be expressed as:

[0026]

[0027] where h target，l ,h target，irepresent the effective links between the base station and the Ith and ith sensing targets respectively; therefore, the mean square cross-correlation pattern between all L radar sensing signals is expressed as P cross , which is represented as:

[0028]

[0029] The objective function is transformed into r ,y r , v rms A function of three variables, including: Calculate the predicted travel time of the direct wave at the i-th shot point:

[0030] Will Substitution have to

[0031] That is, the objective function Φ is a function about x r ,y r , v rms Function of three variables.

[0032] Preferably, in the communication perception integrated system containing type I communication users, the following joint optimization design problem model is established:

[0033]

[0034]

[0035]

[0036]

[0037] C4:P cross ≤ε c ,

[0038] Maximize the minimum beam gain of L sensing targets to achieve better sensing performance; C1 represents the maximum transmission power limit of the base station that must be met during the optimization process; C2 represents the signal-to-noise ratio of the kth communication user in the Ith category must be greater than the minimum signal-to-noise ratio set, where Γ k represents the signal-to-noise ratio of the kth user; C3 represents the qth clutter interference power borne by the system Must be less than the maximum tolerable clutter power η q ; C4 represents the mean square cross-correlation pattern P between all L radar sensor signals cross Must be less than the maximum upper limit ε c ;

[0039] In the communication perception integrated system containing type II communication users, the following joint optimization design problem model is established:

[0040]

[0041]

[0042]

[0043]

[0044] C8:P cross ≤ε c .

[0045] The goal is to maximize the minimum beam gain of L sensing targets to achieve better sensing performance; C5 represents that the optimization process must meet the maximum transmission power limit of the base station; C6 represents the signal-to-noise ratio of the kth communication user of Class II must be greater than the minimum signal-to-noise ratio set, where Γ k represents the signal-to-noise ratio of the kth user; C7 represents the qth clutter interference power borne by the system Must be less than the maximum tolerable clutter power η q ; C8 represents the mean square cross-correlation pattern P between all L radar sensor signals cross Must be less than the maximum upper limit ε c .

[0046] Preferably, the joint optimization design problem model is decomposed into two sub-problems: Sub-problem 1: Under the condition of fixing the reflection beam matrix Φ of the smart reflection surface, optimize the communication signal transmission beam {w k} and radar signal covariance matrix R0;

[0047] Sub-problem 2: In a fixed communication signal transmission beam {w k} and the radar signal covariance matrix R0, optimize the reflection beam matrix Φ of the smart reflection surface;

[0048] Optimize using the alternating optimization algorithm:

[0049] Given the reflection beam matrix Φ of the smart reflection surface, the original problem is transformed into a problem of solving the communication signal transmission beam {w k} and the radar signal covariance matrix R0, which is a typical SDP problem. Convex optimization tools such as CVX are used to obtain the solution of the problem, that is, to obtain the base station's transmission communication signal transmission beam {w k} and radar signal covariance matrix R0;

[0050] Get the base station's transmission communication signal transmission beam {w k} and the radar signal covariance matrix R0, the reflection beam matrix Φ of the smart reflection surface is optimized, the problem is transformed into a convex problem, and the convex optimization tool is used to solve it to obtain the optimal reflection beam matrix Φ.

[0051] Preferably, based on these two sub-problems, the above two processes are repeatedly solved, and an alternating optimization algorithm is used to achieve system performance optimization. The specific optimization process is as follows:

[0052] The above two solving processes, each time the solution to the problem obtained by alternate optimization is the obtained base station's transmission communication signal transmission beam {w k} and the radar signal covariance matrix R0 and the reflection beam matrix Φ of the smart reflective surface must satisfy 1, the minimum signal-to-noise ratio at the communication user to ensure the communication quality, that is, in the formula and where Γ k represents the signal-to-noise ratio of the kth user; if the base station's transmit power threshold is met, the formula is satisfied Where P0 represents the maximum transmission power that the base station can provide; meeting the clutter interference power threshold to achieve a good clutter suppression effect satisfies the formula where η q Represents the maximum clutter power that the system can tolerate; satisfying the cross-correlation pattern restriction between radar signals to achieve a good radar detection effect, that is, satisfying the formula P cross ≤ε c , where ε c Represents the maximum upper limit power of the mutual information between multiple radar signals.

[0053] The present invention also proposes a communication perception integrated equipment based on anti-clutter and intelligent reflective surface assistance, the equipment comprising:

[0054] Communication model, radar perception model, clutter model; the clutter model is expressed as:

[0055]

[0056] Define a new vector represents the effective link between the base station and the qth clutter, where and represent the clutter signal vectors between the base station and the smart reflector to the qth clutter, respectively, and w k represents the information beam vector of the communication user, and R0 represents the covariance matrix of the sensor signal;

[0057] It also includes a joint optimization base station, which sets the active beam of the joint optimization base station, that is, the communication signal transmission beam {wk} and radar signal covariance matrix R0, set the passive beam of the smart reflecting surface, where the reflection beam matrix Φ of the smart reflecting surface; at the maximum transmission power of the base station, ensure the minimum signal-to-noise ratio of two different communication receiver users to ensure the communication quality of the communication users, impose an upper bound of the tolerable clutter power on each clutter scatterer, and constrain the design of the cross-correlation pattern between radar signals. The optimization goal is to maximize the minimum beam gain of multiple sensing targets.

[0058] The present invention introduces intelligent reflective surfaces into the integrated communication and perception technology system to solve the situation where there is no direct path between the ground base station and the target of interest in a cluttered environment, that is, the perception link may be blocked by cluttered scatterers. Specifically, the following situation is taken into account: the integrated communication and perception technology system performs target perception and user communication in a cluttered environment. Intelligent reflective surfaces are deployed to enhance communication signals, suppress interference, and ensure the communication quality of macrocellular users. In addition, the intelligent reflective surface helps to redesign the propagation environment, establish a virtual LoS link, and provide better sensing services for sensing targets blocked by clutter. In addition, the present invention further designs the cross-correlation pattern between radar signals to achieve the best radar sensing effect. And the present invention also specifically considers two types of communication user receivers, which can offset the impact of radar signals on the quality of communication services and cannot eliminate the impact, respectively, to achieve the protection of communication services for different communication users. Therefore, the sensing link of the integrated communication and perception technology system may be blocked by clutter scatterers and can still be sensed and the user communication performance is good. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0060] 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0061] Figure 1 This is a system framework diagram of a communication perception integration method based on anti-clutter and intelligent reflective surface assistance of the present invention;

[0062] Figure 2 The present invention is an algorithm flow chart of a communication perception integration method based on anti-clutter and intelligent reflective surface assistance. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0064] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0065] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0066] The present invention discloses a communication perception integration method based on anti-clutter and intelligent reflective surface assistance, referring to Figure 1-2 The scenario set by the present invention is as follows Figure 1 As shown in the figure, a ground base station is equipped with M>1 antennas, which jointly send communication signals and special radar signals, and receive echo signals to track targets. An intelligent reflector device is equipped with N>1 antenna uniform linear arrays (ULA) and L potential targets. There are Q clutter signals between the ground base station and the sensing target. The clutter set is expressed as These potential targets may be obstructed by clutter objects. There are K downlink communication users, and the set of users is Each user is configured with an antenna. It is assumed that the ground base station knows the global channel conditions and obtains the prior position information of the sensing target in the detection phase to synthesize the required beam pattern.

[0067] The transmission signal x of the ground base station is:

[0068]

[0069] in is the transmit beamforming vector for propagating the communication signal of the kth communication user, is the corresponding communication signal, is a random variable with zero mean and unit variance and the batches are independent of each other. The dedicated sensor signal transmitted by the base station has a mean of zero. We set the covariance matrix of the sensor signal to be At this time, the total power constraint condition sent by the communication sensing integrated technology base station is recorded as:

[0070]

[0071] Where P0 represents the maximum transmission power that the base station can provide.

[0072] First, establish the communication model of the system:

[0073] The communication model is based on the downlink model analysis. The communication user will receive two signals: a direct link from the base station and an indirect link reflected from the smart reflector. The smart reflector is equipped with N>1 reflective elements n∈{1, 2, …, N}, and we define a related reflection beam matrix as represents the phase shift matrix of the N elements of the smart reflector, where φ n ∈(0, 2π], Each communication user is assigned a dedicated communication beam vector. The Kth user will receive signals from the base station and the smart reflector, so the received signal y at the Kth user is k It can be expressed as:

[0074]

[0075] We define a new orientation represents the effective link between the base station and the Kth communication user. Represents the channel matrix of the link from the ground base station to the smart reflector. Represent the communication channel vectors from the ground base station and the smart reflector to the Kth communication user, respectively, [·] H Represents the conjugate transpose operation on the matrix. k ,s k They correspond to the communication signal beam vector at the kth user and the corresponding communication signal, x0 represents the radar sensor signal, Represents the additive Gaussian white noise received at the kth user.

[0076] For the first type of communication user, we define it as type I, which has the ability to eliminate the influence of radar signals. Therefore, for the kth communication user, user k will receive the desired signal beam w k In addition, it will also receive the signal beam of other communication user j Therefore, the signal beams of other communication users will produce spatial interference suppression for the kth user. Therefore, for type I communication users, the signal-to-noise ratio received at user k is It can be expressed as:

[0077]

[0078] where h CU，k represents the effective communication link between the base station and the communication user k, Represents the additive white Gaussian noise interference received by user k.

[0079] The second type of communication user is defined as type II. It does not have the ability to process radar signals, so the radar signal x0 will also affect the communication quality of the kth communication user. Therefore, for type II communication users, the signal-to-noise ratio received at user k is It can be expressed as:

[0080]

[0081] where h CU，k represents the effective communication link between the base station and the communication user k, Represents the additive white Gaussian noise interference received by user k.

[0082] Secondly, establish the radar perception model of the system

[0083] We provide sensing services to potential L targets. The LoS link between the ground base station and the sensing target may be blocked by clutter objects. Therefore, we can use the virtual LoS link created by the smart reflective surface to provide sensing services. In this case, the sensing target will be served by the smart reflective surface. If the sensing target is not blocked by clutter, the ground base station will also provide sensing services. Therefore, the sensing target will be jointly provided with sensing services by the base station and the smart reflective surface. The present invention uses the beam pattern gain at the potential target at the location of interest as the performance measure of the sensing. We assume that the set of potential sensing targets is And the angle of the sensing target relative to the smart reflective surface is {θ1,…,θ l}. The array steering vector at the smart reflective surface is

[0084]

[0085] Assume d IRS It represents the spacing between consecutive reflective elements at the smart reflective surface, and λ represents the wavelength.

[0086] And the communication signal s kand the radar signal x0 can jointly improve the sensing service for the sensing target, so the expected beam pattern gain at the I-th sensing target is expressed as:

[0087]

[0088] One of the newly defined vectors represents the effective link between the base station and the Ith sensor target, where represents the sensing channel vector from the ground base station to the Ith sensing target, w k represents the information beam vector of the communication user, and R0 represents the covariance matrix of the sensor signal.

[0089] When performing target sensing services, we must consider the accuracy of radar detection, so we need to design the cross-correlation pattern between radar signals. First, we consider the cross-correlation of signals between two sensing targets. For the first target with an angle of θ relative to the smart reflector, l The sensing target and the i-th angle relative to the smart reflective surface is θ i The cross-correlation pattern between the sensing targets is expressed as P(θ l ,θ i ), which can be expressed as:

[0090]

[0091] where h target，l ,h target，i represent the effective links between the base station and the Ith and ith sensing targets respectively. Therefore, the mean square cross-correlation pattern between all L radar sensing signals is expressed as P cross , which is represented as:

[0092]

[0093] In order to obtain good radar detection accuracy, we need to design the mean square cross-correlation pattern between radar signals.

[0094] Finally, the clutter model of the system is established

[0095] For the suppression of clutter scatterers, we rely on the prior knowledge of clutter, such as the covariance matrix of clutter for design. We use the covariance matrix of clutter interference to counteract the harmful interference caused by clutter. The power of clutter interference borne by our system is expressed as:

[0096]

[0097] We define a new vector represents the effective link between the base station and the qth clutter, where and represent the clutter signal vectors between the base station and the smart reflector to the qth clutter, respectively, and w k represents the information beam vector of the communication user, and R0 represents the covariance matrix of the sensor signal.

[0098] In the present invention, we jointly optimize the active beam design of the base station (communication signal transmission beam {w k} and radar signal covariance matrix R0) and intelligent reflective surface passive beam design (reflection beam matrix Φ of the intelligent reflective surface). At the maximum transmission power of the base station, the minimum signal-to-noise ratio of two different communication receiver users is guaranteed to ensure the communication quality of the communication users. At the same time, the upper limit of the tolerable clutter power is imposed on each clutter scatterer, and the cross-correlation pattern design between radar signals is constrained. The optimization goal is to maximize the minimum beam gain of multiple sensing targets.

[0099] In the communication perception integrated system with type I communication users, the following joint optimization design problem model is established:

[0100]

[0101]

[0102]

[0103]

[0104] C4:P cross ≤ε c ,

[0105] Our goal is to maximize the minimum beam gain of L sensing targets to achieve better sensing performance. C1 represents the maximum transmission power limit of the base station that must be met during the optimization process; C2 represents the signal-to-noise ratio of the kth communication user in category I. must be greater than the minimum signal-to-noise ratio set, where Γ k represents the signal-to-noise ratio of the kth user; C3 represents the qth clutter interference power borne by the system Must be less than the maximum tolerable clutter power η q ; C4 represents the mean square cross-correlation pattern P between all L radar sensor signals cross Must be less than the maximum upper limit ε c .

[0106] In the communication perception integrated system containing type II communication users, the following joint optimization design problem model is established:

[0107]

[0108]

[0109]

[0110]

[0111] C8:P cross ≤ε c .

[0112] Our goal is to maximize the minimum beam gain of L sensing targets to achieve better sensing performance. C5 represents the maximum transmission power limit of the base station that must be met during the optimization process; C6 represents the signal-to-noise ratio of the kth communication user of class II. must be greater than the minimum signal-to-noise ratio set, where Γ k represents the signal-to-noise ratio of the kth user; C7 represents the qth clutter interference power borne by the system Must be less than the maximum tolerable clutter power η q ; C8 represents the mean square cross-correlation pattern P between all L radar sensor signals cross Must be less than the maximum upper limit ε c .

[0113] The specific optimization plan is as follows:

[0114] In view of the above two different types of communication users, we decompose the original problem into two sub-problems and use the alternating optimization algorithm to optimize them:

[0115] Sub-problem 1: Optimize the communication signal transmission beam {w k} and radar signal covariance matrix R0;

[0116] Sub-problem 2: In a fixed communication signal transmission beam {w k} and the radar signal covariance matrix R0, the reflection beam matrix Φ of the smart reflection surface is optimized.

[0117] First, given the reflection beam matrix Φ of the smart reflection surface, the original problem is transformed into a problem of solving the communication signal transmission beam {w k} and the radar signal covariance matrix R0, which is a typical SDP problem. We can use convex optimization tools such as CVX to obtain the solution of the problem, that is, to obtain the base station's transmission communication signal transmission beam {w k} and radar signal covariance matrix R0.

[0118] Then, after obtaining the base station's transmission communication signal transmission beam {w k} and the radar signal covariance matrix R0, we optimize the reflection beam matrix Φ of the smart reflection surface, transform the problem into a convex problem, and use convex optimization tools to solve it to obtain the optimal reflection beam matrix Φ.

[0119] Based on these two sub-problems, we repeatedly solve the above two processes and use the alternating optimization algorithm to achieve the optimal system performance. The specific optimization process is as follows:

[0120] We repeat the above two solving processes, and each time we optimize the solution of the problem obtained by alternating optimization, that is, the transmission beam {w k} and the radar signal covariance matrix R0 and the reflection beam matrix Φ of the smart reflective surface must satisfy 1, the minimum signal-to-noise ratio at the communication user to ensure the communication quality, that is, in the formula and where Γ k represents the signal-to-noise ratio of the kth user; 2. The transmit power threshold of the base station satisfies the formula Where P0 represents the maximum transmission power that the base station can provide; 3. Satisfy the clutter interference power threshold to achieve good clutter suppression effect, that is, satisfy the formula where η q Represents the maximum clutter power that the system can tolerate; 4. Satisfy the cross-correlation mode restrictions between radar signals to achieve good radar detection effect, that is, satisfy the formula P cross ≤ε c , where ε c Represents the maximum upper limit power of the mutual information between multiple radar signals.

[0121] When the system performance is the minimum beam gain ρ(θ l ) grows slowly to the minimum tolerance limit (the growth between the beam gains of the two objective functions is less than the minimum tolerance limit), we stop the alternating optimization process and output the last solved base station's transmission communication signal transmission beam {w k} and the radar signal covariance matrix R0 and the reflection beam matrix Φ of the smart reflecting surface.

[0122] Example 2

[0123] The present invention proposes a communication perception integrated equipment based on anti-clutter and intelligent reflective surface assistance, the equipment includes:

[0124] Communication model, radar perception model, clutter model; the clutter model is expressed as:

[0125]

[0126] Define a new orientation represents the effective link between the base station and the qth clutter, where and represent the clutter signal vectors between the base station and the smart reflector to the qth clutter, respectively, and w k represents the information beam vector of the communication user, and R0 represents the covariance matrix of the sensor signal;

[0127] It also includes a joint optimization base station, which sets the active beam of the joint optimization base station, that is, the communication signal transmission beam {w k} and radar signal covariance matrix R0, set the passive beam of the smart reflecting surface, where the reflection beam matrix Φ of the smart reflecting surface; at the maximum transmission power of the base station, ensure the minimum signal-to-noise ratio of two different communication receiver users to ensure the communication quality of the communication users, impose an upper bound of the tolerable clutter power on each clutter scatterer, and constrain the design of the cross-correlation pattern between radar signals. The optimization goal is to maximize the minimum beam gain of multiple sensing targets.

[0128] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A communication perception integration method based on anti-clutter and intelligent reflective surface assistance, characterized in that: The method comprises: Establish communication model, radar perception model and clutter model; the clutter model is expressed as: Define a new vector represents the effective link between the base station and the qth clutter, where and represent the clutter signal vectors between the base station and the smart reflector to the qth clutter, respectively, and w k represents the signal beam vector of the communication user, and R0 represents the covariance matrix of the sensor signal; Set the active beam of the joint optimization base station, that is, the communication signal transmission beam {w k } and radar signal covariance matrix R0, set the passive beam of the intelligent reflective surface, where the reflection beam matrix φ of the intelligent reflective surface; at the maximum transmission power of the base station, ensure the minimum signal-to-noise ratio of two different communication receiver users to ensure the communication quality of the communication users, impose an upper bound of the tolerable clutter power on each clutter scatterer, and constrain the design of the cross-correlation pattern between radar signals. The optimization goal is to maximize the minimum beam gain of multiple sensing targets; The communication model is based on the downlink model analysis. The communication user will receive two signals: a direct link from the base station and an indirect link reflected from the smart reflector. The smart reflector is equipped with N>1 reflective elements n∈{1, 2, …, N}, and a related reflection beam matrix is ​​defined as represents the phase shift matrix of the N elements of the smart reflector, where φ n ∈(0, 2π], Each communication user is assigned a dedicated communication beam vector. The kth user will receive signals from the base station and the smart reflector, so the received signal y at the kth user is k is expressed as: Define a new vector represents the effective link between the base station and the kth communication user; Represents the channel matrix of the link from the ground base station to the smart reflector. Represent the communication channel vectors from the ground base station and the smart reflector to the kth communication user, respectively, [·] H represents the conjugate transpose operation of the matrix, w k ,s k They correspond to the communication signal beam vector at the kth user and the corresponding communication signal, x0 represents the radar sensor signal, represents the additive Gaussian white noise received at the kth user; For the first communication user, it is defined as type l, which has the ability to eliminate the influence of radar signals. For the kth communication user, user k will receive the desired signal beam w. k In addition, it will also receive the signal beam of other communication user j Therefore, the signal beams of other communication users will produce spatial interference suppression for the kth user; for type l communication users, the signal-to-noise ratio received at user k is is expressed as: where h CU，k represents the effective communication link between the base station and the communication user k, Represents the additive white Gaussian noise interference received by user k. The second type of communication user is defined as type II. It does not have the ability to process radar signals, so the radar signal x0 will also affect the communication quality of the kth communication user; therefore, for type II communication users, the signal-to-noise ratio received at user k is is expressed as: where h CU，k represents the effective communication link between the base station and the communication user k, Represents the additive white Gaussian noise interference received by user k.

2. According to claim 1, a communication perception integration method based on anti-clutter and intelligent reflective surface assistance is characterized in that: Providing sensing services to potential L targets, the LoS link between the ground base station and the sensing target may be blocked by clutter objects, so the virtual LoS link created by the smart reflective surface is used to provide sensing services. In this case, the sensing target will be served by the smart reflective surface; If the sensing target is not obstructed by clutter, the ground base station will also provide sensing services, so the sensing target will be jointly sensed by the base station and the smart reflective surface; The beam pattern gain at the potential target at the location of interest is used as the performance metric of the sensing; let the set of potential sensing targets be And the angle of the sensing target relative to the smart reflective surface is {θ1,…,θ l }; The array steering vector at the smart reflective surface is Assume d IRS represents the spacing between consecutive reflective elements at the smart reflective surface, λ represents the wavelength, and the communication signal s k Together with the radar signal x0, it improves the sensing service for the sensing target. Therefore, the expected beam pattern gain at the I-th sensing target is expressed as: One of the newly defined vectors represents the effective link between the base station and the Ith sensor target, where represents the sensing channel vector from the ground base station to the Ith sensing target, w k represents the information beam vector of the communication user, and R0 represents the covariance matrix of the sensor signal; Consider the signal cross-correlation between two sensing targets, for the lth sensor with an angle θ relative to the smart reflector l The sensing target and the i-th angle relative to the smart reflective surface is θ i The cross-correlation pattern between the sensing targets is expressed as P(θ l ,θ i ), which is expressed as: where h target,l ,h target,i represent the effective links between the base station and the Ith and ith sensing targets respectively; therefore, the mean square cross-correlation pattern between all L radar sensing signals is expressed as P cross , which is represented as:

3. The communication perception integration method based on anti-clutter and intelligent reflective surface assistance according to claim 2 is characterized in that: In the communication perception integrated system with type l communication users, the following joint optimization design problem model is established: (P1): max minρ(θ l ) C4:P cross ≤ε c Maximize the minimum beam gain of L sensing targets to achieve better sensing performance; C1 represents the maximum transmission power limit of the base station that must be met during the optimization process; C2 represents the signal-to-noise ratio of the kth communication user in the Ith category must be greater than or equal to the minimum signal-to-noise ratio set, where Γ k Represents the minimum signal-to-noise ratio set; C3 represents the qth clutter interference power that the system withstands Must be less than or equal to the maximum tolerable clutter power η q ; C4 represents the mean square cross-correlation pattern P between all L radar sensor signals cross Must be less than or equal to the maximum upper limit ε c ; In the communication perception integrated system containing type II communication users, the following joint optimization design problem model is established: (P2): max minρ(θ l ) C8:P cross ≤ε c The goal is to maximize the minimum beam gain of L sensing targets to achieve better sensing performance; C5 represents that the optimization process must meet the maximum transmission power limit of the base station, P0 represents the maximum transmission power that the base station can provide; C6 represents the signal-to-noise ratio of the kth communication user of Class II must be greater than or equal to the minimum signal-to-noise ratio set, where Γ k Represents the minimum signal-to-noise ratio set; C7 represents the qth clutter interference power that the system withstands Must be less than or equal to the maximum tolerable clutter power η q ; C8 represents the mean square cross-correlation pattern P between all L radar sensor signals cross Must be less than or equal to the maximum upper limit ε c .

4. The communication perception integration method based on anti-clutter and intelligent reflective surface assistance according to claim 3 is characterized in that: The joint optimization design problem model is decomposed into two sub-problems: Sub-problem 1: Under the condition of fixing the reflection beam matrix φ of the smart reflection surface, optimize the communication signal transmission beam {w k } and radar signal covariance matrix R0; Sub-problem 2: In a fixed communication signal transmission beam {w k } and the radar signal covariance matrix R0, optimize the reflection beam matrix φ of the smart reflection surface; Optimize using the alternating optimization algorithm: Given the reflection beam matrix φ of the smart reflection surface, the original problem is transformed into a problem of solving the communication signal transmission beam {ω k } and the radar signal covariance matrix R0, which is a typical SDP problem. The convex optimization tool is used to obtain the solution of the problem, that is, to obtain the base station's transmission communication signal transmission beam {w k } and radar signal covariance matrix R0; Get the base station's transmission communication signal transmission beam {w k } and the radar signal covariance matrix R0, the reflection beam matrix φ of the smart reflection surface is optimized, the problem is transformed into a convex problem, and the convex optimization tool is used to solve it to obtain the optimal reflection beam matrix φ.

5. The communication perception integration method based on anti-clutter and intelligent reflective surface assistance according to claim 4 is characterized in that: Based on these two sub-problems, the solutions are repeatedly solved and the alternating optimization algorithm is used to optimize the system performance. The specific optimization process is as follows: During the solving process, the solution to the problem obtained by each alternating optimization is the transmission beam {w k } and the radar signal covariance matrix R0 and the reflection beam matrix φ of the smart reflective surface must satisfy: the minimum signal-to-noise ratio at the communication user to ensure the communication quality, that is, in the formula and where Γ k Represents the set minimum signal-to-noise ratio; if the base station transmit power threshold is met, the formula is satisfied Where P0 represents the maximum transmission power that the base station can provide; meeting the clutter interference power threshold to achieve a good clutter suppression effect satisfies the formula where η q Represents the maximum clutter power that the system can tolerate; satisfying the cross-correlation pattern restriction between radar signals to achieve a good radar detection effect, that is, satisfying the formula P cross ≤ε c , where ε c Represents the maximum upper limit power of the mutual information between multiple radar signals.

6. A communication perception integrated equipment based on anti-clutter and intelligent reflective surface assistance, characterized in that: The equipment includes: Communication model, radar perception model, clutter model; the clutter model is expressed as: Define a new vector represents the effective link between the base station and the qth clutter, where and represent the clutter signal vectors between the base station and the smart reflector to the qth clutter, respectively, and w k represents the information beam vector of the communication user, and R0 represents the covariance matrix of the sensor signal; It also includes a joint optimization base station, which sets the active beam of the joint optimization base station, that is, the communication signal transmission beam {w k } and radar signal covariance matrix R0, set the passive beam of the intelligent reflective surface, where the reflection beam matrix φ of the intelligent reflective surface; at the maximum transmission power of the base station, ensure the minimum signal-to-noise ratio of two different communication receiver users to ensure the communication quality of the communication users, impose an upper bound of the tolerable clutter power on each clutter scatterer, and constrain the design of the cross-correlation pattern between radar signals. The optimization goal is to maximize the minimum beam gain of multiple sensing targets; The communication model is based on the downlink model analysis. The communication user will receive two signals: a direct link from the base station and an indirect link reflected from the smart reflector. The smart reflector is equipped with N>1 reflective elements n∈{1, 2, …, N}, and a related reflection beam matrix is ​​defined as represents the phase shift matrix of the N elements of the smart reflector, where φ n ∈(0, 2π], Each communication user is assigned a dedicated communication beam vector. The kth user will receive signals from the base station and the smart reflector, so the received signal y at the kth user is k is expressed as: Define a new vector represents the effective link between the base station and the kth communication user; Represents the channel matrix of the link from the ground base station to the smart reflector. Represent the communication channel vectors from the ground base station and the smart reflector to the Kth communication user, respectively, [·] H represents the conjugate transpose operation of the matrix, w k ,s k They correspond to the communication signal beam vector at the kth user and the corresponding communication signal, x0 represents the radar sensor signal, represents the additive Gaussian white noise received at the kth user; The first communication user is defined as type I, which has the ability to eliminate the influence of radar signals. For the kth communication user, user k will receive the desired signal beam w. k In addition, it will also receive the signal beam of other communication user j Therefore, the signal beams of other communication users will produce spatial interference suppression for the kth user; for type I communication users, the signal-to-noise ratio received at user k is is expressed as: where h CU，k represents the effective communication link between the base station and the communication user k, Represents the additive white Gaussian noise interference received by user k; The second type of communication user is defined as type II. It does not have the ability to process radar signals, so the radar signal x0 will also affect the communication quality of the kth communication user; therefore, for type II communication users, the signal-to-noise ratio received at user k is is expressed as: where h CU，k represents the effective communication link between the base station and the communication user k, Represents the additive white Gaussian noise interference received by user k.

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