A working method of a communication perception symbiotic system

Abstract

The present invention provides a working method for a communication perception symbiotic system, which belongs to the field of communication perception integrated architecture and digital signal processing. The method includes establishing a communication signal transmission model based on the communication perception symbiotic system; performing communication reception signal processing at the communication user; establishing a perception signal model in the communication perception symbiotic system; performing perception echo signal processing at the perception node, and using the communication perception integrated signal to provide communication services to Internet of Vehicles users. The perception node uses dimensionality reduction maximum likelihood estimation based on the perception echo signal to achieve perception of the communication user and the perception target; and feeding back the perception results obtained by the perception node to the base station to assist in realizing the design of the downlink communication perception integrated signal at the next moment. The present invention can effectively avoid the problem of perception performance degradation caused by self-interference problems and effectively reduce hardware costs and energy consumption.

Description

Technical Field

[0001] The present invention belongs to the field of communication perception integrated architecture and digital signal processing, and specifically relates to a working method of a communication perception symbiotic system. Background Art

[0002] The sixth generation (6G) of mobile communication networks is expected to achieve high-quality wireless connectivity and high-precision perception. With the recent development of multi-input, multi-output (MIMO) radar and massive MIMO communication technology, radar and communication systems are moving toward higher frequency bands and more antennas. As a result, radar and communication systems are converging in hardware architecture, channel characteristics, and signal processing, creating new opportunities for integrated communication and perception.

[0003] Based on the degree of integration between the communication and perception systems, communication and perception integration can be categorized into two types: (a) radar-communication coexistence; and (b) full-duplex communication radar. Radar-communication coexistence systems utilize prior information to eliminate interference between the communication and radar systems, requiring minimal modification to the existing system hardware architecture. Full-duplex communication radar systems, on the other hand, utilize the same hardware system to transmit integrated communication and perception signals, simultaneously performing both communication and perception functions, further improving spectrum efficiency and reducing hardware costs.

[0004] However, both the existing radar communication coexistence system and full-duplex communication radar system have inherent drawbacks. Radar communication coexistence systems utilize physically separate communication and radar systems, which is costly. Furthermore, since both separate systems must actively transmit their own signals, energy consumption is high and inter-device interference is severe. Full-duplex communication radar systems, due to limited transmit-receive isolation, inevitably suffer from self-interference. Summary of the Invention

[0005] The purpose of the present invention is to propose a working method for a communication perception symbiotic system, which can effectively avoid the problem of perception performance degradation caused by self-interference problems and effectively reduce hardware costs and energy consumption.

[0006] The present invention is achieved through the following technical solutions:

[0007] A working method of a communication-sensing symbiotic system, comprising a base station, a sensing node, a communication user, and a sensing target. The base station is equipped with a uniform planar antenna array to transmit a communication-sensing integrated signal, and the sensing node is equipped with a uniform planar antenna array to receive echoes of the communication-sensing integrated signal reflected by the communication user and the sensing target. The communication user is a strong target, the sensing target is a weak target, and the radar transmission cross-section of the communication user is much larger than that of the sensing target. The working method of the communication-sensing symbiotic system comprises the following steps:

[0008] Step S1: Based on the communication perception symbiotic system, a communication signal transmission model is established, where the communication signal transmission model includes a communication channel model, generation of a communication perception integrated signal, and a communication reception signal at a communication user;

[0009] Step S2: Processing the received communication signal at the communication user to provide communication services to the Internet of Vehicles users using the integrated communication and perception signal.

[0010] Step S3: In the communication perception symbiotic system, a perception signal model is established, where the perception signal model includes a perception link model and a perception echo signal received at a perception node;

[0011] Step S4: The sensing node performs sensing echo signal processing. While using the communication sensing integrated signal to provide communication services to the communication user, the sensing node uses dimensionality reduction maximum likelihood estimation based on the sensing echo signal to achieve perception of the communication user and the sensing target.

[0012] Step S5: Feedback the perception result obtained by the perception node in step S4 to the base station to assist in realizing the downlink communication perception integrated signal design at the next moment.

[0013] Furthermore, in the communication-perception symbiotic system, the communication users include cars or trucks, and the perception targets include drones or pedestrians.

[0014] Furthermore, the step S1 specifically includes the following steps:

[0015] Step S11: Based on the communication perception symbiotic system, a communication channel model is established. The communication channel model is expressed as Where ρ represents the channel gain per unit distance, d bu represents the distance between the base station and the communication user, θ uh and θ uv Respectively represent the azimuth and elevation angles between the base station and the communication user, represents the transmit array response vector at the base station, M h Indicates the number of horizontal antennas in the base station's antenna array, M vIndicates the number of vertical antennas in the antenna array of the base station;

[0016] Step S12: Based on the prior information, the downlink beam direction is set to the position of the communication user and the sensing target obtained at the previous moment, and a downlink communication sensing integrated signal is generated, which is expressed as Among them, s(t) represents the communication symbol at time t, which obeys the Gaussian distribution with mean 0 and variance 1, P represents the base station transmission power, θ th and θ tv represent the azimuth and elevation angles between the base station and the sensing target, respectively, ||·|| F Indicates the F norm operation on the matrix;

[0017] Step S13: Based on the communication channel model established in step S11 and the downlink communication perception integrated signal generated in step S12, a communication reception signal at the communication user is established, which is expressed as y(t)=h H x(t)+n c , where n c represents Gaussian white noise, with a mean of 0 and a variance of [·] H Represents the conjugate transpose operation on the matrix.

[0018] Furthermore, in step S2, the communication user performs communication signal processing including channel estimation, equalization, demodulation and decoding.

[0019] Furthermore, the step S3 specifically includes the following steps:

[0020] Step S31: In the communication perception symbiotic system, a perception link model is established, which includes a base station-communication user-perception node link model. and base station-sensing target-sensing node link model Among them, u With ζ t Denote the radar reflection cross-section of the communication user and the perception target, d us represents the distance between the communication user and the sensing node, d bt Represents the distance between the base station and the sensing target, d ts Represents the distance between the sensing target and the sensing node, and They represent the arrival angle of the communication user's perception signal, that is, the azimuth and elevation angles between the communication user and the perception node, and They represent the arrival angle of the sensing signal of the sensing target, that is, the azimuth and pitch angle between the sensing target and the sensing node, δ s (·) is the receiving array response vector at the sensing node,

[0021] Step S32: Based on the perception link model and the downlink communication perception integrated signal, establish the perception echo signal received at the perception node, expressed as Y r =H su X+H st X+N s , where X = [x(1),…,x(L)] represents the set of downlink signals at L moments, N s Represents a Gaussian white noise matrix, where each element has a mean of 0 and a variance of Gaussian distribution.

[0022] Furthermore, the step S4 specifically includes the following steps:

[0023] Step S41: The communication and perception integrated echo from the perception node is used as interference, and the perception echo signal is rearranged by column quantization, expressed as y r =y u +y t +n s , where y r =vec(Y r ), y u =vec(H su X), y t =vec(H st X), n s =vec(N s ), vec(·) represents the operation of rearranging the matrix into column vectors;

[0024] Step S42: setting a value set of the azimuth angle of the communication user to be estimated relative to the sensing node and a value set of the elevation angle of the communication user to be estimated relative to the sensing node, including setting ranges of the two value sets and intervals traversed within the ranges;

[0025] Step S43: Use the maximum likelihood estimation with dimensionality reduction to traverse the two value sets according to the interval set in step S42, and substitute the angle values ​​obtained in each traversal into the formula Calculate the spatial spectrum. The angle corresponding to the peak of the spatial spectrum is the estimated azimuth of the communication user relative to the sensing node. and pitch angle Right now Among them, max(·) means taking the maximum operation, arg(·) means taking the angle operation corresponding to the function value, represents the value in the set of arrival angles of the perceived echo signal to be estimated, Indicates the azimuth and elevation angles of the communication user relative to the base station at the last moment,

[0026] Step S44: The azimuth of the communication user relative to the sensing node estimated in step S43 and pitch angle Calculate the location of the communication user;

[0027] Step S45: Based on the azimuth angle of the communication user relative to the sensing node and pitch angle Performing spatial domain filtering on the sensed echo signal obtained in step S32 to form a null at the communication user's arrival angle in the beam pattern, thereby eliminating echo interference from the communication user;

[0028] Step S46: Rearrange the spatially filtered perception echo signal obtained in step S45 by column quantization to obtain y′ r , and set the value set of the azimuth angle of the perception target to be estimated relative to the perception node and the value set of the pitch angle of the perception target to be estimated relative to the perception node, including setting the range of the two value sets and the interval of traversal within the range, using dimensionality reduction maximum likelihood estimation, traversing the two value sets according to the set intervals, and substituting the angle values ​​obtained in each traversal into the formula Get the azimuth of the sensing target relative to the sensing node and pitch angle And calculate the position of the perceived target, where represents the value in the set of arrival angles of the perceived echo signal to be estimated, Indicates the azimuth and elevation angles of the target relative to the base station at the last moment.

[0029] Furthermore, the step S5 specifically includes the following steps:

[0030] Step S51: Calculate the azimuth and elevation angles of the communication user and the perception target relative to the base station based on the position of the communication user and the perception target obtained in steps S44 and S46;

[0031] Step S52: Feedback the azimuth angle and elevation angle obtained in step S51 as prior information to the base station to assist in generating a downlink communication sensing integrated signal;

[0032] Step S53: Determine whether the communication and perception tasks are to continue. If so, proceed to step S1 to perform communication transmission and target perception at the next moment. Otherwise, end.

[0033] The present invention has the following beneficial effects:

[0034] 1. The present invention first establishes a communication signal transmission model based on a communication perception symbiotic system, performs communication receiving signal processing at the communication user, and then establishes a perception signal model and obtains a perception echo signal. The perception node realizes perception of the communication user and the perception target based on the perception echo signal, and finally feeds back the perception result obtained by the perception node to the base station as prior information to realize the downlink communication perception integrated signal design. By deploying perception nodes to perceive the echo signal, it is possible to effectively reduce the perception performance degradation caused by the self-interference problem of the full-duplex base station. The perception node uses the communication perception integrated signal to realize the perception function. Compared with the independent deployment of active perception nodes in the prior art, it can avoid the configuration of the transmission link and reduce hardware cost and energy consumption. By feeding back the perception result obtained at the perception node to the base station as prior information to assist in the communication perception integrated signal design, it can effectively improve the downlink beam alignment accuracy. By adopting dimensionality reduction maximum likelihood estimation for the communication user and the perception target respectively, it can effectively improve the positioning accuracy of the perception target. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described in detail below with reference to the accompanying drawings.

[0036] Figure 1 This is an architectural diagram of the communication-aware symbiotic system of the present invention.

[0037] Figure 2 Flowchart of the present invention.

[0038] Figure 3 This is a flow chart of step S4 of the present invention.

[0039] Figure 4 This figure shows the simulation results of positioning communication users and perception targets using the existing multiple signal classification method.

[0040] Figure 5 This is a diagram showing the simulation results of positioning communication users and perception target points using the present invention.

[0041] Figure 6 This is a comparison chart of the estimated root mean square error simulation results of the present invention and the existing multiple signal classification method. DETAILED DESCRIPTION

[0042] like Figure 1As shown in the figure, the communication perception symbiosis system works in the vehicle network scenario, which can realize downlink communication of mobile communication users and accurate positioning of perception targets. The communication perception symbiosis system specifically includes a base station, a perception node, a communication user and a perception target. The base station is equipped with a uniform planar antenna array to send communication perception integrated signals. In the uniform planar antenna array equipped by the base station, the number of horizontal antennas is recorded as M h , the number of longitudinal antennas is recorded as M v , the total number of antennas is recorded as M = M h ×M v The sensing node is equipped with a uniform planar antenna array to receive the communication sensing integrated signal echo reflected by the communication user and the sensing target. The number of horizontal antennas in the uniform planar antenna array equipped by the sensing node is recorded as N h , the number of vertical antennas is recorded as N v , the total number of antennas is recorded as N = N h ×N v , the number of antennas of the communication user is 1, the communication user is considered as a strong target, the sensing target is a weak target, and the radar transmission cross-section of the communication user is much larger than that of the sensing target;

[0043] Specifically in this embodiment, M h =8,M v =8, M=64, N h =8, N v =8, N=8, the three-dimensional coordinates of the base station deployment location, sensing node deployment location, communication user location, and sensing target location are (0,0,20), (0,210,20), (20,200,0), and (-40,160.0), respectively, in meters. The communication user is a car, and the sensing target is a pedestrian.

[0044] like Figure 2 As shown, the working method of the communication-aware symbiotic system includes the following steps:

[0045] Step S1: Based on the communication perception symbiotic system, a communication signal transmission model is established. The communication signal transmission model includes a communication channel model, generation of a communication perception integrated signal, and a communication reception signal at a communication user. Specifically, the model includes the following steps:

[0046] Step S11: Based on the communication perception symbiotic system, a communication channel model is established. The communication channel model is expressed as Where ρ represents the channel gain per unit distance, d bu represents the distance between the base station and the communication user, θ uh and θ uv Respectively represent the azimuth and elevation angles between the base station and the communication user, represents the transmit array response vector at the base station, Specifically in this embodiment, ρ = 1 × 10 -5 ;

[0047] Step S12: Based on the prior information, the downlink beam direction is set to the position of the communication user and the sensing target obtained at the previous moment, and a downlink communication sensing integrated signal is generated, which is expressed as Among them, s(t) represents the communication symbol at time t, which obeys the Gaussian distribution with mean 0 and variance 1, P represents the base station transmission power, θ th and θ tv represent the azimuth and elevation angles between the base station and the sensing target, respectively, ||·|| F Indicates the F-norm operation on the matrix. Specifically in this embodiment, P = 15dBm,

[0048] Step S13: Based on the communication channel model established in step S11 and the downlink communication perception integrated signal generated in step S12, a communication reception signal at the communication user is established, which is expressed as y(t)=h H x(t)+n c , where n c represents Gaussian white noise, with a mean of 0 and a variance of [·] H Indicates that a conjugate transpose operation is performed on the matrix. Specifically in this embodiment,

[0049] Step S2: Processing the received communication signal at the communication user to provide communication services to the Internet of Vehicles users using the integrated communication and perception signal.

[0050] Communication signal processing is performed at the communication user, including physical layer processes such as channel estimation, equalization, demodulation, and decoding.

[0051] Step S3: In the communication-sensing symbiotic system, a sensing signal model is established. The sensing signal model includes a sensing link model and a sensing echo signal received at a sensing node. Specifically, the model includes the following steps:

[0052] Step S31: In the communication perception symbiotic system, a perception link model is established, which includes a base station-communication user-perception node link model. and base station-sensing target-sensing node link model Among them, u With ζ t Denote the radar reflection cross-section of the communication user and the perception target, d us represents the distance between the communication user and the sensing node, d btRepresents the distance between the base station and the sensing target, d ts Represents the distance between the sensing target and the sensing node, and They represent the arrival angle of the communication user's perception signal, that is, the azimuth and elevation angles between the communication user and the perception node, and They represent the arrival angle of the sensing signal of the sensing target, that is, the azimuth and pitch angle between the sensing target and the sensing node, δ s (·) is the receiving array response vector at the sensing node,

[0053] Step S32: Based on the perception link model and the downlink communication perception integrated signal, establish the perception echo signal received at the perception node, expressed as Y r =H su X+H st X+N s , where X = [x(1),…,x(L)] represents the set of downlink signals at L moments, N s Represents a Gaussian white noise matrix, where each element has a mean of 0 and a variance of Gaussian distribution.

[0054] Specifically in this embodiment, u =20dBsm,ζ t =1dBsm, L=1000.

[0055] Step S4: The sensing node performs sensing echo signal processing, using the integrated communication and sensing signal to provide communication services to the communication user (i.e., the Internet of Vehicles user). The sensing node uses dimensionality reduction maximum likelihood estimation based on the sensing echo signal to achieve perception of the communication user and the sensing target.

[0056] Specific steps are as follows Figure 3 As shown, including:

[0057] Step S41: The communication and perception integrated echo from the perception node is used as interference, and the perception echo signal is rearranged by column quantization, expressed as y r =y u +y t +n s , where y r =vec(Y r ), y u =vec(H su X), y t =vec(H stX), n s =vec(N s ), vec(·) represents the operation of rearranging the matrix into column vectors;

[0058] Step S42: setting a value set of the azimuth angle of the communication user to be estimated relative to the sensing node and a value set of the elevation angle of the communication user to be estimated relative to the sensing node, including setting the range of the two value sets and the interval traversed within the range; in this embodiment, the two value sets are both set to -90:0.1:90;

[0059] Step S43: Use the maximum likelihood estimation with dimensionality reduction to traverse the two value sets according to the interval set in step S42, and substitute the two angle values ​​obtained in each traversal into Calculate the spatial spectrum, the angle corresponding to the peak of the spatial spectrum is the estimated azimuth of the communication user relative to the sensing node and pitch angle Right now Among them, max(·) means taking the maximum operation, arg(·) means taking the angle operation corresponding to the function value, represents the value in the set of arrival angles of the perceived echo signal to be estimated, Indicates the azimuth and elevation angles of the communication user relative to the base station at the last moment,

[0060] Step S44: The azimuth of the communication user relative to the sensing node estimated in step S43 and pitch angle Calculate the location of the communication user;

[0061] Step S45: Based on the azimuth angle of the communication user relative to the sensing node and pitch angle Performing spatial domain filtering on the sensed echo signal obtained in step S32 to form a null at the communication user's arrival angle in the beam pattern, thereby eliminating echo interference from the communication user;

[0062] Step S46: Rearrange the spatially filtered perception echo signal obtained in step S45 by column quantization to obtain y′ r , and set the value set of the azimuth angle of the perception target to be estimated relative to the perception node and the value set of the pitch angle of the perception target to be estimated relative to the perception node, including setting the range of the two value sets and the interval of traversal within the range, using dimensionality reduction maximum likelihood estimation, traversing the two value sets according to the set intervals, and substituting the angle values ​​obtained in each traversal into Get the azimuth of the sensing target relative to the sensing node and pitch angle And calculate the position of the perceived target, where represents the value in the set of arrival angles of the perceived echo signal to be estimated, Indicates the azimuth and pitch angle of the sensing target relative to the sensing node at the last moment,

[0063] Step S5: Feedback the sensing result obtained by the sensing node in step S4 to the base station to assist in implementing the downlink communication sensing integrated signal design at the next moment, specifically including the following steps:

[0064] Step S51: Calculate the azimuth and elevation angles of the communication user and the perception target relative to the base station based on the position of the communication user and the perception target obtained in steps S44 and S46;

[0065] Step S52: Feedback the azimuth angle and elevation angle obtained in step S51 as prior information to the base station to assist in generating a downlink communication sensing integrated signal;

[0066] Step S53: Determine whether the communication and perception tasks are to continue. If so, proceed to step S1 to perform communication transmission and target perception at the next moment. Otherwise, end.

[0067] Figure 4 This is the simulation result of positioning communication users and sensing targets using the traditional Multiple Signal Classification (MUSIC) method. Figure 5 This is a diagram showing the simulation results of positioning communication users and perception target points using the present invention. Figure 4 and Figure 5 In the figure, the horizontal axis is the azimuth angle of the communication user and the sensing target relative to the sensing node, in degrees, and the vertical axis is the pitch angle, in degrees. Figure 4 and Figure 5 It can be seen from the figure that both the present invention and the MUSIC method can effectively estimate the angle of strong targets, but for estimating weak targets, the present invention can achieve higher accuracy.

[0068] Figure 6 In the figure, the horizontal axis is the base station transmit power, ranging from -10dBm to 30dBm with an interval of 5dBm, and the vertical axis is the root mean square error (RMSE) between the estimated perceived target angle and the true angle, in degrees. Figure 6In [1], the Cramér-Rao lower bound (CRLB) represents the theoretical minimum mean square error of unbiased parameter estimation. It can be seen that at higher transmit powers, the RMSE of the proposed method for perceived target angle estimation can approach the CRLB, and the perceptual performance of the proposed method is superior to that of the traditional MUSIC method.

[0069] The above description is merely a preferred embodiment of the present invention and therefore cannot be used to limit the scope of the present invention. In other words, equivalent changes and modifications made according to the scope of the patent application and the contents of the specification should still fall within the scope of the patent of the present invention.

Claims

1. A method for operating a communication-aware symbiotic system, characterized by: The communication and perception symbiotic system includes a base station, a perception node, a communication user, and a perception target. The base station is equipped with a uniform planar antenna array to transmit a communication and perception integrated signal. The perception node is equipped with a uniform planar antenna array to receive the communication and perception integrated signal echo reflected by the communication user and the perception target. The communication user is a strong target, and the perception target is a weak target. The radar transmission cross-section of the communication user is much larger than that of the perception target. The working method of the communication and perception symbiotic system includes the following steps: Step S1: Based on the communication perception symbiotic system, a communication signal transmission model is established, where the communication signal transmission model includes a communication channel model, generation of a communication perception integrated signal, and a communication reception signal at a communication user; Step S2: Processing the received communication signal at the communication user to provide communication services to the Internet of Vehicles users using the integrated communication and perception signal. Step S3: In the communication perception symbiotic system, a perception signal model is established, where the perception signal model includes a perception link model and a perception echo signal received at a perception node; Step S4: The sensing node performs sensing echo signal processing. While using the communication sensing integrated signal to provide communication services to the communication user, the sensing node uses dimensionality reduction maximum likelihood estimation based on the sensing echo signal to achieve perception of the communication user and the sensing target. Step S5: Feedback the perception result obtained by the perception node in step S4 to the base station to assist in realizing the downlink communication perception integrated signal design at the next moment.

2. The method of claim 1, wherein: In the communication-perception symbiotic system, the communication users include cars or trucks, and the perception targets include drones or pedestrians.

3. The method of claim 2, wherein: The step S1 specifically includes the following steps: Step S11: Based on the communication perception symbiotic system, a communication channel model is established. The communication channel model is expressed as Where ρ represents the channel gain per unit distance, d bu represents the distance between the base station and the communication user, θ uh and θ uv Respectively represent the azimuth and elevation angles between the base station and the communication user, represents the transmit array response vector at the base station, M h Indicates the number of horizontal antennas in the base station's antenna array, M v Indicates the number of vertical antennas in the antenna array of the base station; Step S12: Based on the prior information, the downlink beam direction is set to the position of the communication user and the sensing target obtained at the previous moment, and a downlink communication sensing integrated signal is generated, which is expressed as Among them, s(t) represents the communication symbol at time t, which obeys the Gaussian distribution with mean 0 and variance 1, P represents the base station transmission power, θ th and θ tv represent the azimuth and elevation angles between the base station and the sensing target, respectively, ||·|| F Indicates the F norm operation on the matrix; Step S13: Based on the communication channel model established in step S11 and the downlink communication perception integrated signal generated in step S12, a communication reception signal at the communication user is established, which is expressed as y(t)=h H x(t)+n c , where n c represents Gaussian white noise, with a mean of 0 and a variance of [·] H Represents the conjugate transpose operation on the matrix.

4. The method for operating a communication-aware symbiotic system according to claim 3, wherein: In step S2, the communication user performs communication signal processing including channel estimation, equalization, demodulation and decoding.

5. The method for operating a communication-aware symbiotic system according to claim 4, characterized in that: The step S3 specifically includes the following steps: Step S31: In the communication perception symbiotic system, a perception link model is established, which includes a base station-communication user-perception node link model. and base station-sensing target-sensing node link model Among them, u With ζ t Denote the radar reflection cross-section of the communication user and the perception target, d us represents the distance between the communication user and the sensing node, d bt Represents the distance between the base station and the sensing target, d ts Represents the distance between the sensing target and the sensing node, and They represent the arrival angle of the communication user's perception signal, that is, the azimuth and elevation angles between the communication user and the perception node, and They represent the arrival angle of the sensing signal of the sensing target, that is, the azimuth and pitch angle between the sensing target and the sensing node, δ s (·) is the receiving array response vector at the sensing node, Step S32: Based on the perception link model and the downlink communication perception integrated signal, establish the perception echo signal received at the perception node, expressed as Y r =H su X+H st X+N s , where X = [x(1),…,x(L)] represents the set of downlink signals at L moments, N s Represents a Gaussian white noise matrix, where each element has a mean of 0 and a variance of Gaussian distribution.

6. The method for operating a communication-aware symbiotic system according to claim 5, characterized in that: The step S4 specifically includes the following steps: Step S41: The communication and perception integrated echo from the perception node is used as interference, and the perception echo signal is rearranged by column quantization, expressed as y r =y u +y t +n s , where y r =vec(Y r ), y u =vec(H su X), y t =vec(H st X), n s =vec(N s ), vec(·) represents the operation of rearranging the matrix into column vectors; Step S42: setting a value set of the azimuth angle of the communication user to be estimated relative to the sensing node and a value set of the elevation angle of the communication user to be estimated relative to the sensing node, including setting ranges of the two value sets and intervals traversed within the ranges; Step S43: Use the maximum likelihood estimation with dimensionality reduction to traverse the two value sets according to the interval set in step S42, and substitute the angle values ​​obtained in each traversal into the formula Calculate the spatial spectrum. The angle corresponding to the peak of the spatial spectrum is the estimated azimuth of the communication user relative to the sensing node. and pitch angle Right now Among them, max(·) means taking the maximum operation, arg(·) means taking the angle operation corresponding to the function value, represents the value in the set of arrival angles of the perceived echo signal to be estimated, Indicates the azimuth and elevation angles of the communication user relative to the base station at the last moment, Step S44: The azimuth of the communication user relative to the sensing node estimated in step S43 and pitch angle Calculate the location of the communication user; Step S45: Based on the azimuth angle of the communication user relative to the sensing node and pitch angle Performing spatial domain filtering on the sensed echo signal obtained in step S32 to form a null at the communication user's arrival angle in the beam pattern, thereby eliminating echo interference from the communication user; Step S46: Rearrange the spatially filtered perception echo signal obtained in step S45 by column quantization to obtain y′ r , and set the value set of the azimuth angle of the perception target to be estimated relative to the perception node and the value set of the pitch angle of the perception target to be estimated relative to the perception node, including setting the range of the two value sets and the interval of traversal within the range, using dimensionality reduction maximum likelihood estimation, traversing the two value sets according to the set intervals, and substituting the angle values ​​obtained in each traversal into the formula Get the azimuth of the sensing target relative to the sensing node and pitch angle And calculate the position of the perceived target, where represents the value in the set of arrival angles of the perceived echo signal to be estimated, Indicates the azimuth and elevation angles of the target relative to the base station at the last moment.

7. The method for operating a communication-aware symbiotic system according to claim 6, characterized in that: The step S5 specifically includes the following steps: Step S51: Calculate the azimuth and elevation angles of the communication user and the perception target relative to the base station based on the position of the communication user and the perception target obtained in steps S44 and S46; Step S52: Feedback the azimuth angle and elevation angle obtained in step S51 as prior information to the base station to assist in generating a downlink communication sensing integrated signal; Step S53: Determine whether the communication and perception tasks are to continue. If so, proceed to step S1 to perform communication transmission and target perception at the next moment. Otherwise, end.

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