Working method for symbiotic sensing and communication system

Abstract

The present invention belongs to the fields of integrated sensing and communication architectures and digital signal processing. Provided is a working method for a symbiotic sensing and communication system. The method comprises: on the basis of a symbiotic sensing and communication system, establishing a communication signal transmission model; executing received communication signal processing at a communication user; establishing a sensing signal model in the symbiotic sensing and communication system; executing sensing echo signal processing at a sensing node, and while an integrated sensing and communication signal is used for providing a communication service to an Internet-of-Vehicles user, the sensing node realizing, on the basis of sensing echo signals, the sensing of the communication user and a sensing target using dimension-reduced maximum likelihood estimation; and feeding back to a base station a sensing result obtained by the sensing node, so as to assist in realizing the design of a downlink integrated sensing and communication signal for a subsequent moment. The present invention can effectively avoid the problem of degradation of the sensing performance caused by self-interference, and significantly reduce hardware costs and energy consumption.

Description

Method for operating a communication and perception symbiotic system TECHNICAL FIELD

[0001] The present application belongs to the field of communication and perception integrated architecture and digital signal processing, and particularly relates to a method for operating a communication and perception symbiotic system. BACKGROUND

[0002] The sixth generation (6G) mobile communication network is expected to realize high-quality wireless connection and high-precision perception. In recent years, with the development of multi-input multi-output (MIMO) radar and large-scale MIMO communication technology, radar systems and communication systems are developing towards higher frequency bands and more antennas. Therefore, the hardware architecture, channel characteristics and signal processing procedures of radar and communication systems tend to be consistent, providing new opportunities for communication and perception integration.

[0003] According to the degree of integration of communication systems and perception systems, communication and perception integration can be divided into two categories: (a) radar communication coexistence; (b) full-duplex communication radar. Radar communication coexistence systems strive to eliminate interference between communication systems and radar systems according to prior information, so there is less modification to the existing system hardware architecture. Full-duplex communication radar systems use the same hardware system to send communication and perception integrated signals, and simultaneously realize communication and perception functions to further improve spectral efficiency and reduce hardware costs.

[0004] However, the above existing radar communication coexistence systems and full-duplex communication radar systems each have their own inherent defects. Radar communication coexistence systems use physically separate communication systems and radar systems, which are costly. And because both separate systems need to actively send their own signals, energy consumption is high and interference between devices is serious. Full-duplex communication radar systems have an unavoidable self-interference problem due to the limitation of transmit-receive isolation.

[0005] SUMMARY

[0006] The purpose of the present application is to provide a method for operating a communication and perception symbiotic system, which can effectively avoid the problem of decreased perception performance caused by self-interference, and effectively reduce hardware costs and energy consumption.

[0007] The present application is implemented by the following technical solutions:

[0008] The application discloses a working method of a communication and perception symbiotic system, and relates to the field of communication and perception symbiotic systems.

[0009] Step S1, based on the communication and perception symbiotic system, a communication signal transmission model is established, the communication signal transmission model comprising a communication channel model, generation of a communication and perception integrated signal and a communication receiving signal at a communication user;

[0010] Step S2, a communication receiving signal processing is performed at the communication user, and communication services are provided for a vehicle networking user by using the communication and perception integrated signal;

[0011] Step S3, in the communication and perception symbiotic system, a perception signal model is established, the perception signal model comprising a perception link model and a perception echo signal received at a perception node;

[0012] Step S4, a perception echo signal processing is performed at the perception node, and the perception node performs perception on the communication user and the perception target by using a dimension reduction maximum likelihood estimation based on the perception echo signal while providing communication services for the communication user by using the communication and perception integrated signal;

[0013] Step S5, the perception result obtained by the perception node in step S4 is fed back to the base station, and the downlink communication and perception integrated signal design in the next moment is assisted.

[0014] Further, in the communication and perception symbiotic system, the communication user comprises an automobile or a truck, and the perception target comprises an unmanned aerial vehicle or a pedestrian.

[0015] Further, the step S1 specifically comprises the following steps.

[0016] Step S11, based on the communication and perception symbiotic system, a communication channel model is established, the communication channel model being expressed as Wherein, ρ represents a channel gain at a unit distance, d bu represents a distance between the base station and the communication user, θ uh and θ uv respectively represent an azimuth angle and an elevation angle between the base station and the communication user, represents a transmitting array response vector at the base station, M h represents a transverse antenna quantity in the antenna array of the base station, Mv represents the number of longitudinal antennas in the antenna array of the base station;

[0017] Step S12, based on the prior information, set the downlink beam direction as the bearing of the communication user and the sensing target obtained at the previous time, generate a downlink communication and sensing integrated signal, denoted as where s(t) represents the communication symbol at time t, which obeys a Gaussian distribution with mean 0 and variance 1, P represents the transmit power of the base station, θ th and θ tv respectively represent the azimuth angle and the elevation angle between the base station and the sensing target, and ‖g‖ F represents the F-norm operation on the matrix;

[0018] Step S13, based on the communication channel model established in step S11 and the downlink communication and sensing integrated signal generated in step S12, establish a communication receiving signal at the communication user, denoted as y(t) = h H x(t) + n c , where n c represents a Gaussian white noise with mean 0 and variance [g] H represents the conjugate transpose operation on the matrix.

[0019] Further, in the step S2, the communication signal processing performed at the communication user includes channel estimation, equalization, demodulation and decoding.

[0020] Further, the step S3 specifically includes the following steps:

[0021] Step S31, in the communication and sensing coexistence system, establish a sensing link model, which includes a base station-communication user-sensing node link model and a base station-sensing target-sensing node link model where ζ u and ζ t respectively represent the radar cross section area of the communication user and the sensing 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 respectively represent the sensing signal angle of arrival of the communication user, i.e., the azimuth angle and the elevation angle between the communication user and the sensing node, and respectively represent the sensing signal angle of arrival of the sensing target, i.e., the azimuth angle and the elevation angle between the sensing target and the sensing node, δ s (·) is the receiving array response vector at the sensing node,

[0022] Step S32, based on the sensing link model and the downlink communication sensing integrated signal, the received sensing echo signal at the sensing node is established, denoted as Y r = H su X + H st X + N s , wherein X = [x(1), L, x(L)] represents a set of downlink signals at L time instants, N s represents a Gaussian white noise matrix, wherein each element obeys a Gaussian distribution with mean 0 and variance .

[0023] Further, the step S4 specifically comprises the following steps:

[0024] Step S41, the communication sensing integrated echo from the sensing node is taken as interference, the sensing echo signal is column vectorized and rearranged, denoted as y r = y u + y t + n s , wherein y r = vec(Y r ), y u = vec(H su X), y t = vec(H st X), n s = vec(N s ), and vec(g) represents a column vectorization and rearrangement operation on a matrix;

[0025] Step S42, a set of values of the azimuth angle of the communication user relative to the sensing node to be estimated and a set of values of the elevation angle of the communication user relative to the sensing node to be estimated are set, including setting the range of the two sets of values and the interval of traversal in the range respectively;

[0026] Step S43, a dimension reduction maximum likelihood estimation is adopted, the two sets of values are traversed according to the interval set in step S42 respectively, and each time the angle value obtained by traversal is substituted into the formula to calculate the spatial spectrum, and the angle corresponding to the spatial spectrum peak is the estimated azimuth angle and elevation angle of the communication user relative to the sensing node, that is , wherein max(g) represents a maximum operation, arg(g) represents an angle operation of taking the function value, and denotes a value in the set of estimated angles of arrival of the perception echo signal, denotes the azimuth and elevation angles of the communication user relative to the base station at the previous time instant,

[0027] Step S44, according to the azimuth and elevation angles of the communication user relative to the perception node estimated in step S43, Step S44, according to the azimuth and elevation angles of the communication user relative to the perception node estimated in step S43, Step S44, according to the azimuth and elevation angles of the communication user relative to the perception node estimated in step S43,

[0028] Step S45, according to the azimuth and elevation angles of the communication user relative to the perception node, Step S45, according to the azimuth and elevation angles of the communication user relative to the perception node, Step S45, according to the azimuth and elevation angles of the communication user relative to the perception node,

[0029] Step S46, the spatially filtered perception echo signal obtained in step S45 is column-wise vectorized and rearranged to obtain y′ r and set the value set of the estimated azimuth angle of the perception target relative to the perception node and the value set of the estimated elevation angle of the perception target relative to the perception node, including setting the range of the two value sets and the interval of traversal in the range, using dimension reduction maximum likelihood estimation, traversing the two value sets according to the set interval, and substituting the angle value obtained each time into the formula Step S46, the spatially filtered perception echo signal obtained in step S45 is column-wise vectorized and rearranged to obtain y′ Step S46, the spatially filtered perception echo signal obtained in step S45 is column-wise vectorized and rearranged to obtain y′ Step S46, the spatially filtered perception echo signal obtained in step S45 is column-wise vectorized and rearranged to obtain y′ denotes a value in the set of estimated angles of arrival of the perception echo signal, denotes the azimuth and elevation angles of the communication user relative to the base station at the previous time instant,

[0030] Further, the step S5 specifically includes the following steps:

[0031] Step S51, according to the position of the communication user and the position of the perception target obtained in steps S44 and S46, the azimuth and elevation angles of the communication user and the perception target relative to the base station are calculated.

[0032] Step S52, the azimuth and elevation angles obtained in step S51 are fed back to the base station as prior information to assist in generating a downlink communication and perception integrated signal.

[0033] Step S53, it is judged whether the communication and perception tasks continue to be executed, if yes, step S1 is entered to perform the communication transmission and target perception at the next time instant, otherwise the process is ended.

[0034] The present application has the following beneficial effects:

[0035] The present application firstly establishes a communication signal transmission model based on a communication and perception symbiotic system, performs communication receiving signal processing at a communication user, secondly establishes a perception signal model and obtains a perception echo signal, and 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 downlink communication and perception integrated signal design; first, the present application can effectively reduce the decline of perception performance caused by self-interference of the full-duplex base station by deploying the perception node to perceive the echo signal, and the perception node realizes the perception function by using the communication and perception integrated signal, which can avoid equipping the transmission link and reduce the hardware cost and energy consumption compared with the existing technology of independently deploying the active perception node; second, the present application can effectively improve the downlink beam alignment accuracy by feeding back the perception result obtained at the perception node to the base station as prior information to assist the communication and perception integrated signal design; third, the present application can effectively improve the positioning accuracy of the perception target by using dimension reduction maximum likelihood estimation for the communication user and the perception target respectively. BRIEF DESCRIPTION OF DRAWINGS

[0036] The present application will be further described in detail below with reference to the accompanying drawings.

[0037] Fig. 1 is an architecture diagram of the communication and perception symbiotic system of the present application.

[0038] Fig. 2 is a flowchart of the present application.

[0039] Fig. 3 is a flowchart of step S4 of the present application.

[0040] Fig. 4 is a positioning simulation result diagram of the communication user and the perception target by using the existing multiple signal classification method.

[0041] Fig. 5 is a positioning simulation result diagram of the communication user and the perception target point by using the present application.

[0042] Fig. 6 is a comparison diagram of the estimation root mean square error simulation results of the present application and the existing multiple signal classification method. DETAILED DESCRIPTION

[0043] As shown in Fig. 1, the present embodiment discloses a communication and perception symbiotic system working in a vehicle-to-everything scene, which can realize downlink communication of a mobile communication user and accurate positioning of a perception target. The communication and perception symbiotic system specifically comprises 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 a communication and perception integrated signal. In the uniform planar antenna array of the base station, the number of transverse antennas is denoted as M h , and the number of longitudinal antennas is denoted as M v, the total number of antennas is denoted as M=M h ×M v , the perception node is equipped with a uniform planar antenna array to receive the communication and perception integrated signal echoes reflected by the communication user and the perception target, among the uniform planar antenna array equipped by the perception node, the number of transverse antennas is denoted as N h , the number of longitudinal antennas is denoted as N v , and the total number of antennas is denoted 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, and the perception target is a weak target, and the radar cross section of the communication user is much larger than that of the perception target;

[0044] In the 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 position, the perception node deployment position, the communication user position, and the perception target position 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 perception target is a pedestrian. The communication user can also be a truck, and the perception target can also be a drone.

[0045] As shown in FIG. 2, the working method of the communication and perception symbiotic system in the embodiment includes the following steps:

[0046] Step S1, based on the communication and perception symbiotic system, a communication signal transmission model is established, which includes a communication channel model, generation of a communication and perception integrated signal, and a communication receiving signal at a communication user, and specifically includes the following steps:

[0047] Step S11, based on the communication and perception symbiotic system, a communication channel model is established, which is denoted as Wherein, 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 represent the azimuth angle and the elevation angle between the base station and the communication user, represents the transmitting array response vector at the base station, In the embodiment, ρ=1×10 -5 ;

[0048] Step S12, based on prior information, the downlink beam direction is set to the azimuth of the communication user and the perception target obtained at the previous time, and a downlink communication and perception integrated signal is generated, which is denoted as where s(t) denotes the communication symbol at time t, which follows a Gaussian distribution with mean 0 and variance 1, P denotes the base station transmit power, θ th and θ tv denote the azimuth and elevation angle between the base station and the sensing target, respectively, ‖g‖ F denotes the F-norm operation on a matrix, and in this embodiment, P = 15 dBm,

[0049] Step S13, based on the communication channel model established in step S11 and the downlink communication-sensing integrated signal generated in step S12, a communication reception signal at the communication user is established, denoted as y(t) = h H x(t) + n c , where n c denotes a Gaussian white noise with mean 0 and variance [g] H denotes the conjugate transpose operation on a matrix, and in this embodiment,

[0050] Step S2, the communication reception signal processing is performed at the communication user, realizing the provision of communication services for the vehicle networking user by using the communication-sensing integrated signal.

[0051] The communication signal processing at the communication user includes channel estimation, equalization, demodulation, decoding, and other physical layer processes.

[0052] Step S3, in the communication-sensing symbiotic system, a sensing signal model is established, which includes a sensing link model and a sensing echo signal received at the sensing node, specifically including the following steps:

[0053] Step S31, in the communication-sensing symbiotic system, a sensing link model is established, which includes a base station-communication user-sensing node link model and a base station-sensing target-sensing node link model where ζ u and ζ t denote the radar reflection cross-sectional area of the communication user and the sensing target, respectively, d us denotes the distance between the communication user and the sensing node, d bt denotes the distance between the base station and the sensing target, d ts denotes the distance between the sensing target and the sensing node, and denote the sensing signal angle of arrival of the communication user, i.e., the azimuth and elevation angle between the communication user and the sensing node, and respectively represent the angle of arrival of the perception signal of the perception target, i.e. the azimuth angle and the elevation angle between the perception target and the perception node, δ s (·) is the receiving array response vector at the perception node,

[0054] Step S32, based on the perception link model and the downlink communication-perception integrated signal, the perception echo signal received at the perception node is established, denoted as Y r = H su X + H st X + N s , wherein X = [x(1), L, x(L)] represents a set of downlink signals at L time instants, N s represents a Gaussian white noise matrix, wherein each element obeys a Gaussian distribution with a mean of 0 and a variance of .

[0055] In this embodiment, ζ u = 20 dBsm, ζ t = 1 dBsm, and L = 1000.

[0056] Step S4, the perception echo signal processing is performed at the perception node, and the communication service for the communication user (i.e. the vehicle networking user) is provided by using the communication-perception integrated signal, and the perception node realizes the perception of the communication user and the perception target based on the perception echo signal by using the reduced dimension maximum likelihood estimation;

[0057] The specific steps are shown in FIG. 3, which include:

[0058] Step S41, the communication-perception integrated echo from the perception node is taken as interference, the perception echo signal is rearranged as a column vector, denoted as y r = y u + y t + n s , wherein y r = vec(Y r ), y u = vec(H su X), y t = vec(H st X), n s = vec(N s ), and vec(g) represents the column vector rearrangement operation of the matrix;

[0059] Step S42, set 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 respectively and the interval in the range; in the embodiment, the two value sets are both set as -90:0.1:90;

[0060] Step S43, using dimension reduction maximum likelihood estimation, traversing the two value sets according to the interval set in step S42 respectively, and the two angle values obtained each time are substituted into to calculate the spatial spectrum, and the angle corresponding to the peak of the spatial spectrum is the estimated azimuth angle of the communication user relative to the sensing node and the elevation angle that is wherein, max(g) represents the maximum operation, arg(g) represents the function value corresponding angle operation, represents the value in the estimated sensing echo signal angle set, represents the azimuth angle and the elevation angle of the communication user relative to the base station at the last moment,

[0061] Step S44, according to the azimuth angle of the communication user relative to the sensing node estimated in step S43 and the elevation angle the position of the communication user is calculated;

[0062] Step S45, according to the azimuth angle of the communication user relative to the sensing node and the elevation angle the sensing echo signal obtained in step S32 is subjected to spatial filtering to form a null in the beam pattern at the communication user arrival angle, eliminating the echo interference from the communication user;

[0063] Step S46, the sensing echo signal subjected to spatial filtering obtained in step S45 is subjected to column vectorization rearrangement to obtain y' r and set a value set of the azimuth angle of the sensing target to be estimated relative to the sensing node and a value set of the elevation angle of the sensing target to be estimated relative to the sensing node, including setting the range of the two value sets respectively and the interval in the range, using dimension reduction maximum likelihood estimation, traversing the two value sets according to the set interval respectively, and the angle value obtained each time is substituted into to obtain the azimuth angle of the sensing target relative to the sensing node and the elevation angle and the position of the sensing target is calculated, wherein, represents the value in the estimated sensing echo signal angle set, denotes the azimuth and elevation of the target relative to the sensing node at the last time instant,

[0064] Step S5, the sensing result obtained by the sensing node in step S4 is fed back to the base station to assist the design of the downlink communication and sensing integrated signal at the next time instant, and specifically includes the following steps:

[0065] Step S51, the azimuth and elevation of the communication user and the target relative to the base station are calculated according to the position of the communication user and the position of the target obtained in steps S44 and S46;

[0066] Step S52, the azimuth and elevation obtained in step S51 are fed back to the base station as prior information to assist the generation of the downlink communication and sensing integrated signal;

[0067] Step S53, it is judged whether the communication and sensing tasks continue to be executed, if yes, step S1 is entered to perform the communication transmission and target sensing at the next time instant, otherwise the process is ended.

[0068] FIG. 4 is a simulation result diagram of the positioning of the communication user and the target by using the traditional Multiple Signal Classification (MUSIC) method, and FIG. 5 is a simulation result diagram of the positioning of the communication user and the target by using the present application. In FIGS. 4 and 5, the horizontal coordinates are the azimuths of the communication user and the target relative to the sensing node, and the units are degrees, and the vertical coordinates are the elevations, and the units are degrees. It can be seen from FIGS. 4 and 5 that the azimuths of the strong targets can be effectively estimated by both the present application and the MUSIC method, but for the weak targets, the present application can obtain higher precision.

[0069] In FIG. 6, the horizontal coordinate is the base station transmission power, and the value range is from -10 dBm to 30 dBm with an interval of 5 dBm, and the vertical coordinate is the Root Mean Square Error (RMSE) between the estimated target angle and the true angle, and the unit is degree. In FIG. 6, the Cramér-Rao lower bound (CRLB) represents the theoretical minimum mean square error of the unbiased parameter estimation. It can be seen that at a higher transmission power, the RMSE of the target angle estimation by the present application can approach the CRLB, and the sensing performance of the present application is superior to that of the traditional MUSIC method.

[0070] The above description is only a preferred embodiment of the present application, and therefore cannot limit the range of the present application, that is, equivalent changes and modifications made according to the patent application scope and the content of the specification should still be within the scope of the present application. Industrial applicability

[0071] The working method of the communication and perception symbiotic system can effectively reduce the decline of perception performance caused by the self-interference problem of the full-duplex base station by deploying the perception node to perceive the echo signal. The perception node uses the communication and perception integrated signal to realize the perception function. Compared with the active perception node independently deployed in the prior art, the perception node can avoid being equipped with a transmitting link, reduce the hardware cost and energy consumption, and has good industrial applicability.

Claims

1. A method for operating a communication-sensing symbiotic system, characterized in that: The communication-sensing symbiotic system includes a base station, sensing nodes, communication users, and sensing targets. The base station is equipped with a uniform planar antenna array to transmit integrated communication-sensing signals. The sensing nodes are equipped with uniform planar antenna arrays to receive the echoes of the integrated communication-sensing signals reflected by the communication users and sensing targets. The communication users are strong targets, and the sensing targets are weak targets. The radar cross-section of the communication users is much larger than that of the sensing targets. The operation method of the communication-sensing symbiotic system includes the following steps: Step S1: Based on the communication sensing symbiotic system, establish a communication signal transmission model, which includes a communication channel model, the generation of integrated communication sensing signals, and the communication reception signals at the communication user. Step S2: The communication user performs communication signal processing to provide communication services to vehicle network users using integrated communication and sensing signals. Step S3: In the communication-sensing symbiotic system, establish a sensing signal model, which includes a sensing link model and sensing echo signals received at sensing nodes. Step S4: At the sensing node, sensing echo signal processing is performed. While providing communication services to communication users using the integrated communication and sensing signal, the sensing node uses dimensionality-reduced maximum likelihood estimation based on the sensing echo signal to realize the perception of communication users and sensing targets. Step S5: Feed back the sensing results obtained by the sensing node in step S4 to the base station to assist in the design of the integrated downlink communication sensing signal for the next moment.

2. The working method of a communication sensing symbiotic system according to claim 1, characterized in that: In the communication-sensing symbiotic system, the communication users include cars or trucks, and the sensing targets include drones or pedestrians.

3. The working method of a communication sensing symbiotic system according to claim 2, characterized in that: Step S1 specifically includes the following steps: Step S11: Based on the communication-aware symbiotic system, establish a communication channel model, which is represented as follows: Where ρ represents the channel gain per unit distance, and d bu θ represents the distance between the base station and the communication user. uh and θ uv These represent the azimuth and elevation angles between the base station and the communication user, respectively. This represents the transmit array response vector at the base station. M h M represents the number of lateral antennas in the base station's antenna array. v This indicates the number of longitudinal antennas in the base station's antenna array; Step S12: Based on prior information, set the downlink beam direction to the azimuth of the communication user and the sensing target obtained at the previous time, and generate a downlink communication and sensing integrated signal, represented as... Where s(t) represents the communication symbol at time t, which follows a Gaussian distribution with mean 0 and variance 1, P represents the base station transmit power, and θ th and θ tv Representing the azimuth and elevation angles between the base station and the sensing target, respectively, ||g|| F This represents the operation of taking the F norm of a matrix; Step S13: Based on the communication channel model established in step S11 and the downlink communication sensing integrated signal generated in step S12, establish the communication received signal at the communication user, expressed as y(t) = h H x(t)+n c , where n c This represents Gaussian white noise with a mean of 0 and a variance of . [g] H This indicates that the matrix is ​​being transposed using the conjugate method.

4. The working method of a communication sensing symbiotic system according to claim 3, characterized in that: In step S2, the communication user performs communication signal processing including channel estimation, equalization, demodulation, and decoding.

5. The working method of a communication sensing symbiotic system according to claim 4, characterized in that: Step S3 specifically includes the following steps: Step S31: In the communication-sensing symbiotic system, establish a sensing link model, which includes a base station-communication user-sensing node link model. Base station-sensing target-sensing node link model Where, ζ u With ζ t Let d represent the radar cross-sections of the communication user and the sensing target, respectively. us d represents the distance between the communication user and the sensing node. bt d represents the distance between the base station and the target being sensed. ts This represents the distance between the perceived target and the perceived node. and These represent the angle of arrival of the sensing signal from the communication user, i.e., the azimuth and elevation angles between the communication user and the sensing node. and δ represents the angle of arrival of the sensing signal of the sensing target, i.e., the azimuth and elevation angles between the sensing target and the sensing node. s (·) represents the receiver array response vector at the sensing node. Step S32: Based on the sensing link model and the integrated downlink communication sensing signal, establish the sensing echo signal received at the sensing node, denoted as Y. r =H su X+H st X+N s ,in, X = [x(1), L, x(L)] represents the set of downlink signals at L times, and N s Let represent a Gaussian white noise matrix, where each element follows a mean of 0 and a variance of . The Gaussian distribution.

6. The working method of a communication sensing symbiotic system according to claim 5, characterized in that: Step S4 specifically includes the following steps: Step S41: Treat the integrated communication and sensing echo from the sensing node as interference, and rearrange the sensing echo signal by column vectorization, represented 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(g) represents the operation of rearranging the matrix into column vectors; Step S42: Set the set of values ​​for the azimuth angle of the communication user to be estimated relative to the sensing node and the set of values ​​for the elevation angle of the communication user to be estimated relative to the sensing node, including setting the range of the two sets of values ​​and the interval for traversing within the range. Step S43: Using dimensionality reduction maximum likelihood estimation, traverse the two value sets at the intervals set in step S42, and substitute the angle value obtained in each traversal into the formula. The spatial spectrum is calculated, and the angle corresponding to the peak of the spatial spectrum is the estimated azimuth angle of the communication user relative to the sensing node. With pitch angle Right now Here, max(g) represents the maximum operation, and arg(g) represents the angle operation corresponding to the function value. This represents the value in the set of angles of arrival of the sensed echo signal to be estimated. This represents the azimuth and elevation angles of the communication user relative to the base station at the previous moment. Step S44: Based on the azimuth angle of the communication user relative to the sensing node estimated in step S43. With pitch angle The location of the communication user is calculated; Step S45: Based on the azimuth angle of the communication user relative to the sensing node. With pitch angle The sensed echo signal obtained in step S32 is subjected to spatial filtering to form nulls at the angle of arrival of the communication user in the beam diagram, thereby eliminating echo interference from the communication user. Step S46: Rearrange the spatially filtered sensing echo signal obtained in step S45 by column vectorization to obtain y′. r The algorithm sets two sets of values ​​for the azimuth angle of the target relative to the sensing node and the elevation angle of the target relative to the sensing node. It defines the ranges for these two sets and the interval for traversing them. A dimensionality-reduced maximum likelihood estimation method is used, and the two sets are traversed at the defined intervals. The angle value obtained in each traversal is substituted into the formula. Obtain the azimuth angle of the target relative to the sensing node. With pitch angle And calculate the position of the perceived target, where, This represents the value in the set of angles of arrival of the sensed echo signal to be estimated. This represents the azimuth and elevation angles of the sensed target relative to the base station at the previous moment.

7. The working method of a communication sensing symbiotic system according to claim 6, characterized in that: Step S5 specifically includes the following steps: Step S51: Based on the positions of the communication user and the sensing target obtained in steps S44 and S46, calculate the azimuth and elevation angles of the communication user and the sensing target relative to the base station. Step S52: Feed the azimuth and elevation angles obtained in step S51 back to the base station as prior information to assist in generating the downlink communication sensing integrated signal. Step S53: Determine whether the communication and perception tasks should continue. If yes, proceed to step S1 to perform the next moment's communication transmission and target perception; otherwise, end.

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