A multi-base station cooperative ISAC method combining active and passive sensing

Abstract

The present invention discloses a method for multi-base station collaborative ISAC for combined active and passive sensing, comprising the following steps: 1. constructing a multi-base station collaborative ISAC; 2. obtaining the achievable rates of downlink communication users and uplink communication users and the signal-to-interference-and-noise ratio of combined active and passive sensing; 3. constructing a joint beamforming and transmission power optimization model; 4. transforming the joint beamforming and transmission power optimization model three times to obtain a three-transformed joint beamforming and transmission power optimization model; and 5. adopting a stepwise convex approximation, a penalty function algorithm, and a fractional programming method to iteratively obtain an optimized value for the three-transformed joint beamforming and transmission power optimization model. While ensuring sensing requirements and power, the present invention maximizes the total rate of downlink and uplink transmissions by optimizing the communication and sensing transmit beamforming matrix, the receive beamforming vector corresponding to the uplink communication user, and the power allocation budget, thereby assisting in optimizing the communication process.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) ISAC technology, and in particular relates to a multi-base station collaborative ISAC method combining active and passive sensing. Background Art

[0002] Integrated Sensing and Communication (ISAC) refers to a technology that uses the same frequency band and hardware for both perception and communication. This technology is considered a key candidate for the next generation of mobile communications. Multi-base station collaborative ISAC, a new technology, has emerged as a promising approach to integrating perception and communication. From a communication perspective, multi-base station collaborative ISAC effectively ensures high-quality communication through joint signal transmission. From a perception perspective, multi-base station collaborative ISAC enables multi-angle perception, providing rich perception information, improving perception accuracy, and expanding the perception atmosphere.

[0003] In active sensing mode, full-duplex base stations experience self-interference between the transmitting and receiving antennas. In passive sensing mode, time asynchrony between the transmitting and receiving base stations can occur. These factors significantly degrade system performance. By combining active and passive sensing, we can effectively mitigate self-interference in full-duplex master base stations and time asynchrony between the transmitting and receiving base stations, while also enhancing sensing accuracy and reliability.

[0004] Therefore, a multi-base station collaborative ISAC method that combines active and passive sensing is needed. While ensuring sensing requirements and power, it maximizes the total rate of downlink and uplink transmission by optimizing the communication and sensing transmit beamforming matrix, the receive beamforming vector corresponding to the uplink communication user, and the power allocation budget, thereby facilitating the auxiliary optimization of the communication process. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned prior art and provide a method for multi-base station collaborative ISAC with combined active and passive perception. The method has simple steps and reasonable design. While ensuring the perception requirements and power, it maximizes the total rate of downlink and uplink transmission by optimizing the communication and perception transmit beamforming matrix, the receive beamforming vector corresponding to the uplink communication user, and the power allocation budget, thereby facilitating the auxiliary optimization of the communication process.

[0006] To solve the above technical problems, the present invention adopts a technical solution: a multi-base station cooperative ISAC method combining active and passive sensing, the method comprising the following steps:

[0007] Step 1: Build multi-base station collaborative ISAC:

[0008] The multi-base station cooperation ISAC includes a primary base station, a secondary base station, a target and a communication user. The number of the secondary base stations is The primary base station and the secondary base station simultaneously detect the target and are responsible for uplink and downlink communications of communication users, wherein the communication users include multiple downlink communication users and multiple uplink communication users; wherein the primary base station is equipped with transmit antennas and receiving antennas, each secondary base station is equipped with receiving antennas, the downlink communication users and uplink communication users are both equipped with single antennas, and the number of downlink communication users is , the number of uplink communication users is , 、 、 、 、 and All are integers; the primary base station actively senses the target's echo signal, and the secondary base station passively senses the target's reflected signal;

[0009] Step 2: Obtain the achievable rate of downlink communication users and uplink communication users and the joint active and passive sensing signal to interference and noise ratio; The achievable rate for downlink communication users is , No. The achievable rate for an uplink communication user is , the signal-to-interference-noise ratio of the combined active and passive sensing is , , ;

[0010] Step 3: Build a joint beamforming and transmission power optimization model:

[0011] A computer is used to maximize the sum of the achievable rates of all communication users, with the goal of ensuring the sensing service requirements and the power budget of the primary base station and uplink communication users as constraints. A joint beamforming and transmission power optimization model is constructed as follows:

[0012] ;in, represents the joint active and passive sensing beam receiving matrix, Indicates the The communication transmission beamforming matrix corresponding to the downlink communication users is: represents the sensing transmit beamforming covariance matrix, and , represents the sensing transmit beamforming matrix, Indicates the The receiving beamforming vector corresponding to the uplink communication user, Indicates the The transmission power of each uplink communication user, represents the predefined joint active and passive sensing signal-to-interference-noise ratio threshold, represents the trace of the matrix, represents the total power budget of the master base station, Indicates the Maximum power budget for uplink communication users; Indicates universal quantifier; Indicates constraints; Indicates the maximum value;

[0013] Step 4: transform the joint beamforming and transmission power optimization model three times to obtain a three-transformed joint beamforming and transmission power optimization model;

[0014] Step 5: Use stepwise convex approximation, penalty function algorithm and fractional programming method to iteratively obtain the optimized value of the joint beamforming and transmission power optimization model after three conversions.

[0015] Furthermore, in step 2, the specific process is as follows:

[0016] Step 201: According to , get the SNR of downlink communication users ;in, Indicates the main base station to the Communication channels for downlink communication users, represents the conjugate transpose, Indicates the The communication transmission beamforming matrix corresponding to the downlink communication users is: The value is ,and , Indicates the Uplink communication user to the Downlink communication user channels, Indicates the Noise variance at each downlink communication user; represents the square of the modulus; DL represents the downlink communication link;

[0017] Step 202: , get the The signal-to-noise ratio of uplink communication users ; Among them, UL represents the uplink communication link, Indicates the The receiving beamforming vector corresponding to the uplink communication user, , Indicates the The receiving beamforming vector corresponding to the uplink communication user to the main base station, Indicates the Uplink communication user to the The receive beamforming vector corresponding to the secondary base station, is a positive integer, and , represents transpose, Indicates the Uplink communication channels for uplink communication users, , Indicates the Communication channels from uplink communication users to the main base station, Indicates the Uplink communication user to the Communication channels of secondary base stations, Indicates the The transmission power of an uplink communication user, Indicates the Uplink communication channels for uplink communication users, and , represents the composite interference channel, and , represents the combined sensing channel, , represents the active sensing channel, Indicates the passive sensing channels, represents the combined self-interference channel, , represents the residual self-interference channel, express Middle Rank Elements of the column, , Indicates the remaining self-interference power of the primary base station, represents a natural constant, represents the imaginary unit, represents pi, Indicates the primary base station The transmitting antenna and The distance between the receiving antennas, , , Indicates wavelength; Indicates the zero matrices, and The dimension is ; represents the square of the F norm, represents the square of the 2-norm, Indicates the The noise variance of uplink communication users at the base station is, represents the combined beamforming matrix;

[0018] Step 203: According to , get the The achievable rate for downlink communication users ;

[0019] according to , get the The achievable rate for uplink communication users ;

[0020] Step 204: , and obtain the signal-to-interference-noise ratio of the joint active and passive sensing ;in, represents the joint active and passive sensing beam receiving matrix, and , Indicates that the master base station actively senses the receiving beamforming matrix, Indicates the The passive sensing receive beamforming matrix of the secondary base stations, , represents the corresponding beamforming matrix for communication, represents the perceived noise variance; Represents the identity matrix.

[0021] Furthermore, in step four, the specific process is as follows:

[0022] Step 401: Use computer to set The intermediate variable corresponding to the downlink communication user is ,and ;in, Indicates the The first intermediate quantity corresponding to downlink communication users, and , , represents the rank of the matrix, Every element in is greater than or equal to zero; Indicates the The second intermediate quantity corresponding to downlink communication users, and , Indicates the The first intermediate quantity corresponding to downlink communication users, and , , Every element in is greater than or equal to zero;

[0023] Set The intermediate variable corresponding to the uplink communication user is ,and , Indicates the The first intermediate quantity corresponding to the uplink communication users, and ;

[0024] Set the third intermediate variable , ;in, represents the perceived noise;

[0025] Step 402: Combine active and passive sensing signal to interference and noise ratio In step 401, the intermediate variables are substituted into the joint beamforming and transmission power optimization model in step 3 to perform a conversion, thereby obtaining the converted joint beamforming and transmission power optimization model as follows:

[0026] ;in, Every element in is greater than or equal to zero, Satisfy the Hermitian matrix;

[0027] Step 403: Use computer to Simplify and get , let the fourth intermediate variable , and for the fourth intermediate variable In the The first iteration The first intermediate quantity corresponding to downlink communication users Hedi Iterative perceptual transmit beamforming covariance matrix Perform a first-order Taylor expansion at ,and ,but ;

[0028] right Simplify and get , let the fifth intermediate variable , and the fifth intermediate variable is The first iteration The first intermediate quantity corresponding to downlink communication users Hedi Iterative perceptual transmit beamforming covariance matrix Perform a first-order Taylor expansion at the position to obtain the second first-order Taylor expansion ,and ,but ;in, represents the sixth intermediate variable, and ; The seventh intermediate variable, and ; represents the eighth intermediate variable, and ; represents the ninth intermediate variable, and ;in, is a natural number;

[0029] Step 404: Use computer to set The intermediate variables corresponding to downlink communication users Convert to The lower limit intermediate variable corresponding to the downlink communication user ,and ;

[0030] Set The intermediate variables corresponding to the uplink communication users Convert to The lower limit intermediate variable corresponding to the uplink communication user ,and ;

[0031] Step 405: Use a computer to perform a second conversion on the joint beamforming and transmission power optimization model after the first conversion to obtain a second converted joint beamforming and transmission power optimization model as follows:

[0032] ;

[0033] Step 406: Use a computer to constrain the non-convex rank-one Equivalent conversion to , and convert the optimization objective into ;in, represents the nuclear norm, represents the 2-norm, is the penalty factor, and ;

[0034] Step 407: Use a computer to use a stepwise convex approximation method to The first iteration The first intermediate quantity corresponding to downlink communication users At, right Perform a first-order Taylor expansion to obtain the third first-order Taylor expansion ,and ; represent The eigenvector corresponding to the maximum eigenvalue of ;

[0035] Step 408: Using a computer, perform a third conversion on the twice-converted joint beamforming and transmission power optimization model to obtain the three-converted joint beamforming and transmission power optimization model, as follows:

[0036] .

[0037] Furthermore, in step five, the specific process is as follows:

[0038] Step 501: Initialize the sensing transmit beamforming matrix, The communication transmission beamforming matrix corresponding to the downlink communication user, the receiving beamforming vector corresponding to the uplink communication user and the After obtaining the first optimal value of the joint active and passive sensing beam receiving matrix, the stepwise convex approximation and penalty function algorithm are used to iterate the joint beamforming and transmission power optimization model after three conversions to obtain the first iteration sensing transmit beamforming matrix. and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ;

[0039] Step 502: Transform the first iteration's sensing transmit beamforming matrix and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users Substituting the transformed joint beamforming and transmission power optimization model into the model, an updated joint beamforming and transmission power optimization model is obtained;

[0040] Step 503: Use the fractional programming method to process the updated joint beamforming and transmission power optimization model to obtain the first iteration of the The receiving beamforming vector corresponding to the uplink communication user and the first iteration The transmission power of uplink communication users ;

[0041] Step 504: Transform the first iteration's sensing transmit beamforming matrix , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , the first iteration The receiving beamforming vector corresponding to the uplink communication user and the first iteration The transmission power of uplink communication users Substitute the objective function in step 3 to obtain the sum of the achievable rates of all communicating users in the first iteration;

[0042] Step 505: According to the method of step 501 to step 504, based on 、 、 、 Execute the next iteration and repeat it many times to get the The sum of the achievable rates of all communicating users in iteration Hedi The sum of the achievable rates of all communicating users in iteration ;in, is a positive integer;

[0043] Step 506: If , then The perceptual transmit beamforming matrix of the iteration , No. The first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , No. The first iteration The receiving beamforming vector corresponding to the uplink communication user Hedi The first iteration The transmission power of uplink communication users As the optimized value, otherwise, perform the next iteration according to the method from step 501 to step 504 until the optimized value is obtained; wherein, is a positive integer, Indicates the judgment termination value.

[0044] Furthermore, in step 501, the specific process is as follows:

[0045] Step 5011: Initialize the sensing transmit beamforming matrix to , initialize the The communication transmission beamforming matrix corresponding to the downlink communication users is: , initialize the The receiving beamforming vector corresponding to the uplink communication user is and initialize the The transmission power of an uplink communication user is ; and get the initialized sensing transmit beamforming covariance matrix and the initialization The first intermediate quantity corresponding to downlink communication users ; Wherein, in step 403 Take zero, in initialization and At first-order Taylor expansion;

[0046] Step 5012: Use the computer to set the first auxiliary amount ,and , the second auxiliary amount ,and , then the matrix The eigenvector corresponding to the maximum eigenvalue of is the first optimal value of the joint active and passive sensing beam receiving matrix ;

[0047] Step 5013: The first optimal value of the combined active and passive sensing beam receiving matrix , initialize the The receiving beamforming vector corresponding to the uplink communication user is and initialize the The transmission power of an uplink communication user is Next, set the initial penalty factor , using the stepwise convex approximation and penalty function algorithm, the first iteration of the joint beamforming and transmission power optimization model after three conversions is performed to obtain the first intermediate iteration of the sensing transmit beamforming matrix and the first intermediate iteration The communication transmission beamforming matrix corresponding to the downlink communication users ;

[0048] Step 5014: The communication transmission beamforming matrix corresponding to the downlink communication users Substitute and judge Is it true? If not, let , execute step 5015; if it is established, the sensing transmit beamforming matrix of the first intermediate iteration is and the first intermediate iteration The communication transmission beamforming matrix corresponding to the downlink communication users As the sensing transmit beamforming matrix of the first iteration and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ;in, Indicates the first intermediate iterative update value, represents the update coefficient; Indicates the maximum termination value;

[0049] Step 5015: Update the value of the first intermediate iteration Input, follow the method of step 5013 and step 5014 to perform the next iteration until Establish the corresponding sensing transmit beamforming matrix of the g-th intermediate iteration and the g-th intermediate iteration The communication transmission beamforming matrix corresponding to the downlink communication users As the sensing transmit beamforming matrix of the first iteration and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ; Wherein, g is a positive integer greater than 1.

[0050] Furthermore, in step 502, the specific process is as follows:

[0051] The first iteration of the sensing transmit beamforming matrix Assign to , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users Assign to , substitute into the transformed joint beamforming and transmission power optimization model, and combine with the third constraint of the joint beamforming and transmission power optimization model in step 3 to obtain the updated joint beamforming and transmission power optimization model as follows:

[0052] ;

[0053] Step 503, the specific process is as follows:

[0054] Step 5031: Use a computer to convert the target function using Lagrange dual transformation The objective function is ;in, Represents the introduced auxiliary variable one, Represents the introduced auxiliary variable 2;

[0055] Step 5032: Use a computer to convert objective function 1 into objective function 2 using the quadratic conversion of fractional programming. ;in, Represents the introduction of auxiliary variable three, Represents the introduction of auxiliary variable four, represents the real part, indicates conjugation; represents the square of the modulus;

[0056] Step 5033: Use a computer to set the first introduced variable , , the second variable is introduced , ;

[0057] Step 5034, initialize the The receiving beamforming vector corresponding to the uplink communication user is and initialize the The transmission power of an uplink communication user is Next, we use a computer to make the objective function about If the derivative is 0, we get the first iteration The first optimization value of downlink communication users and the first iteration The first optimization value of uplink communication users ;in, , ;

[0058] Step 5035: Use a computer to make the objective function 2 about If the derivative is 0, we get the first iteration The second optimal value of downlink communication users and the first iteration The second optimal value of uplink communication users ;in, , ;

[0059] Step 5036, initialize the The transmission power of uplink communication users and the first iteration The first optimization value of downlink communication users , the first iteration The first optimization value of uplink communication users The first iteration The second optimal value of downlink communication users and the first iteration The second optimal value of uplink communication users Assign to 、 、 、 , and substitute it into the objective function 2 and simplify it to get the objective function 3 ;in, represents the introduction of auxiliary variable five, and , Represents the introduction of auxiliary variable six, , represents the first residual constant term;

[0060] Step 5037: Use a computer to calculate the objective function The derivative of the derivative is equal to 0, so we get ; The first iteration The receive beamforming vector corresponding to the uplink communication user;

[0061] Step 5038: The receiving beamforming vector corresponding to the uplink communication user , the first iteration The first optimization value of uplink communication users , the first iteration The first optimization value of downlink communication users , the first iteration The second optimal value of downlink communication users and the first iteration The second optimal value of uplink communication users Substitute into objective function 2 and simplify to get objective function 4 ;in, represents the introduced auxiliary variable seven, and , represents the auxiliary variable eight introduced, and , represents the second residual constant term;

[0062] Step 5039: Use a computer to convert the updated joint beamforming and transmission power optimization model into a convex problem model as follows:

[0063] ;in, represents the auxiliary variable nine introduced, and , represents the auxiliary variable introduced, and ;

[0064] Step 503A: Use a computer to solve the convex problem model using the CVX toolbox to obtain the first iteration. The transmission power of uplink communication users .

[0065] Furthermore, in step 505, the specific process is as follows:

[0066] Step 5051: Based on the first iteration of the sensing transmit beamforming matrix , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , the first iteration The receiving beamforming vector corresponding to the uplink communication user and the first iteration The transmission power of uplink communication users , Take 1, and perform the next iteration according to steps 501 to 503A, repeat multiple times, and get the The perceptual transmit beamforming matrix of the first iteration, The first iteration The communication transmission beamforming matrix corresponding to the downlink communication user, The receiving beamforming vector corresponding to the uplink communication user of the iteration The first iteration The transmission power of each uplink communication user;

[0067] Step 5052: Based on The perceptual transmit beamforming matrix of the first iteration, The first iteration The communication transmission beamforming matrix corresponding to the downlink communication user, The receiving beamforming vector corresponding to the uplink communication user of the iteration The first iteration The transmission power of the uplink communication user is obtained by The sum of the achievable rates of all communicating users in iteration .

[0068] Compared with the prior art, the present invention has the following advantages:

[0069] 1. The method of the present invention has simple steps and is easy to implement. By jointly designing transmit beamforming, power allocation, and receive beamforming vector configuration corresponding to uplink communication users, with the goal of maximizing the sum of uplink and downlink achievable rates, a joint active and passive perception framework is used to improve communication capabilities while ensuring perception performance.

[0070] 2. The present invention adopts a step-by-step convex approximation method and a first-order Taylor expansion to transform the joint beamforming and transmission power optimization model three times to obtain the three-transformed joint beamforming and transmission power optimization model, so that the three-transformed joint beamforming and transmission power optimization model is converted to convex optimization, thereby realizing subsequent iterative solution.

[0071] 3. The present invention decouples the joint beamforming and transmission power optimization model into sub-problems to reduce the complexity of variable coupling. Specifically, the perception reception vector is first solved based on the generalized Rayleigh entropy. Under the given reception beamforming vector and transmission power corresponding to the uplink communication user, relaxation technology, continuous convex approximation method and penalty function algorithm are introduced to transform the joint beamforming and transmission power optimization model after three transformations into sub-problems that can be solved iteratively, and the perception transmission beamforming matrix of the first iteration is obtained. and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ;

[0072] Then given the first iteration of the perceptual transmit beamforming matrix and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , the fractional programming method is introduced to transform the updated joint beamforming and transmission power optimization model into a sub-problem that can be solved iteratively, and the first iteration is obtained. The receiving beamforming vector corresponding to the uplink communication user ;

[0073] Finally, given the first iteration The receiving beamforming vector corresponding to the uplink communication user Next, solve the convex problem model and get the first iteration The transmission power of uplink communication users By constructing a hierarchical iterative optimization strategy, efficient coordination of multi-variable optimization is achieved, which significantly reduces the computational complexity while ensuring the convergence of the algorithm, providing a feasible path for system performance optimization.

[0074] 4. By combining active and passive sensing, the present invention can effectively suppress the self-interference of the full-duplex master base station and the time asynchrony problem between the transmitting base station and the receiving base station through time alignment and joint signal processing between the active and passive sensing signals. At the same time, it can also enhance the perception accuracy and reliability, so that the joint beamforming and transmission power optimization model is more in line with the actual situation, which is conducive to generalized use and more practical.

[0075] In summary, the method of the present invention has simple steps and a reasonable design. While ensuring perception requirements and power, it maximizes the total rate of downlink and uplink transmission by optimizing the communication and perception transmit beamforming matrix, the receive beamforming vector corresponding to the uplink communication user, and the power allocation budget, thereby facilitating the auxiliary optimization of the communication process.

[0076] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0078] like Figure 1 A method for multi-base station cooperative ISAC with combined active and passive sensing is shown, comprising the following steps:

[0079] Step 1: Build multi-base station collaborative ISAC:

[0080] The multi-base station cooperation ISAC includes a primary base station, a secondary base station, a target and a communication user. The number of the secondary base stations is The primary base station and the secondary base station simultaneously detect the target and are responsible for uplink and downlink communications of communication users, wherein the communication users include multiple downlink communication users and multiple uplink communication users; wherein the primary base station is equipped with transmit antennas and receiving antennas, each secondary base station is equipped with receiving antennas, the downlink communication users and uplink communication users are both equipped with single antennas, and the number of downlink communication users is , the number of uplink communication users is , 、 、 、 、 and All are integers; the primary base station actively senses the target's echo signal, and the secondary base station passively senses the target's reflected signal;

[0081] Step 2: Obtain the achievable rate of downlink communication users and uplink communication users and the joint active and passive sensing signal to interference and noise ratio; The achievable rate for downlink communication users is , No. The achievable rate for an uplink communication user is , the signal-to-interference-noise ratio of the combined active and passive sensing is , , ;

[0082] Step 3: Build a joint beamforming and transmission power optimization model:

[0083] A computer is used to maximize the sum of the achievable rates of all communication users, with the goal of ensuring the sensing service requirements and the power budget of the primary base station and uplink communication users as constraints. A joint beamforming and transmission power optimization model is constructed as follows:

[0084] ;in, represents the joint active and passive sensing beam receiving matrix, Indicates the The communication transmission beamforming matrix corresponding to the downlink communication users is: represents the sensing transmit beamforming covariance matrix, and , represents the sensing transmit beamforming matrix, Indicates the The receiving beamforming vector corresponding to the uplink communication user, Indicates the The transmission power of an uplink communication user, represents the predefined joint active and passive sensing signal-to-interference-noise ratio threshold, represents the trace of the matrix, represents the total power budget of the master base station, Indicates the Maximum power budget for uplink communication users; It indicates universal quantifier; Indicates constraints; Indicates the maximum value;

[0085] Step 4: transform the joint beamforming and transmission power optimization model three times to obtain a three-transformed joint beamforming and transmission power optimization model;

[0086] Step 5: Use stepwise convex approximation, penalty function algorithm and fractional programming method to iteratively obtain the optimized value of the joint beamforming and transmission power optimization model after three conversions.

[0087] In this embodiment, the specific process of step 2 is as follows:

[0088] Step 201: According to , get the SNR of downlink communication users ;in, Indicates the main base station to the Communication channels for downlink communication users, represents the conjugate transpose, Indicates the The communication transmission beamforming matrix corresponding to the downlink communication users is: The value is ,and , Indicates the Uplink communication user to the Downlink communication user channels, Indicates the Noise variance at each downlink communication user; represents the square of the modulus; DL represents the downlink communication link;

[0089] Step 202: , get the The signal-to-noise ratio of uplink communication users ; Among them, UL represents the uplink communication link, Indicates the The receiving beamforming vector corresponding to the uplink communication user, , Indicates the The receiving beamforming vector corresponding to the uplink communication user to the main base station, Indicates the Uplink communication user to the The receive beamforming vector corresponding to each secondary base station is: is a positive integer, and , represents transpose, Indicates the Uplink communication channels for uplink communication users, , Indicates the Communication channels from uplink communication users to the main base station, Indicates the Uplink communication user to the Communication channels of secondary base stations, Indicates the The transmission power of each uplink communication user, Indicates the Uplink communication channels for uplink communication users, and , represents the composite interference channel, and , represents the combined sensing channel, , represents the active sensing channel, Indicates the passive sensing channels, represents the combined self-interference channel, , represents the residual self-interference channel, express Middle Rank Elements of the column, , Indicates the remaining self-interference power of the primary base station, represents a natural constant, represents the imaginary unit, represents pi, Indicates the primary base station The transmitting antenna and The distance between the receiving antennas, , , Indicates wavelength; Indicates the zero matrices, and The dimension is ; represents the square of the F norm, represents the square of the 2-norm, Indicates the The noise variance of uplink communication users at the base station is, represents the combined beamforming matrix;

[0090] Step 203: According to , get the The achievable rate for downlink communication users ;

[0091] according to , get the The achievable rate for uplink communication users ;

[0092] Step 204: , and obtain the signal-to-interference-noise ratio of the joint active and passive sensing ;in, represents the joint active and passive sensing beam receiving matrix, and , Indicates that the master base station actively senses the receiving beamforming matrix, Indicates the The passive sensing receive beamforming matrix of the secondary base stations, , represents the corresponding beamforming matrix for communication, represents the perceived noise variance; Represents the identity matrix.

[0093] In this embodiment, the specific process of step 4 is as follows:

[0094] Step 401: Use computer to set The intermediate variable corresponding to the downlink communication user is ,and ;in, Indicates the The first intermediate quantity corresponding to downlink communication users, and , , represents the rank of the matrix, Every element in is greater than or equal to zero; Indicates the The second intermediate quantity corresponding to downlink communication users, and , Indicates the The first intermediate quantity corresponding to downlink communication users, and , , Every element in is greater than or equal to zero;

[0095] Set The intermediate variable corresponding to the uplink communication user is ,and , Indicates the The first intermediate quantity corresponding to the uplink communication users, and ;

[0096] Set the third intermediate variable , ;in, represents the perceived noise;

[0097] Step 402: Combine active and passive sensing signal to interference and noise ratio In step 401, the intermediate variables are substituted into the joint beamforming and transmission power optimization model in step 3 to perform a conversion, thereby obtaining the converted joint beamforming and transmission power optimization model as follows:

[0098] ;in, Every element in is greater than or equal to zero, Satisfy the Hermitian matrix;

[0099] Step 403: Use computer to Simplify and get , let the fourth intermediate variable , and for the fourth intermediate variable In the The first iteration The first intermediate quantity corresponding to downlink communication users Hedi Iterative perceptual transmit beamforming covariance matrix Perform a first-order Taylor expansion at ,and ,but ;

[0100] right Simplify and get , let the fifth intermediate variable , and the fifth intermediate variable is The first iteration The first intermediate quantity corresponding to downlink communication users Hedi Iterative perceptual transmit beamforming covariance matrix Perform a first-order Taylor expansion at the position to obtain the second first-order Taylor expansion ,and ,but ;in, represents the sixth intermediate variable, and ; The seventh intermediate variable, and ; represents the eighth intermediate variable, and ; represents the ninth intermediate variable, and ;in, is a natural number;

[0101] Step 404: Use computer to set The intermediate variables corresponding to downlink communication users Convert to The lower limit intermediate variable corresponding to the downlink communication user ,and ;

[0102] Set The intermediate variables corresponding to the uplink communication users Convert to The lower limit intermediate variable corresponding to the uplink communication user ,and ;

[0103] Step 405: Use a computer to perform a second conversion on the joint beamforming and transmission power optimization model after the first conversion to obtain a second converted joint beamforming and transmission power optimization model as follows:

[0104] ;

[0105] Step 406: Use a computer to constrain the non-convex rank-one Equivalent conversion to , and convert the optimization objective into ;in, represents the nuclear norm, represents the 2-norm, is the penalty factor, and ;

[0106] Step 407: Use a computer to use a stepwise convex approximation method to The first iteration The first intermediate quantity corresponding to downlink communication users At, right Perform a first-order Taylor expansion to obtain the third first-order Taylor expansion ,and ; represent The eigenvector corresponding to the maximum eigenvalue of ;

[0107] Step 408: Using a computer, perform a third conversion on the twice-converted joint beamforming and transmission power optimization model to obtain the three-converted joint beamforming and transmission power optimization model, as follows:

[0108] .

[0109] In this embodiment, the specific process of step five is as follows:

[0110] Step 501: Initialize the sensing transmit beamforming matrix, The communication transmission beamforming matrix corresponding to the downlink communication user, the receiving beamforming vector corresponding to the uplink communication user and the After obtaining the first optimal value of the joint active and passive sensing beam receiving matrix, the stepwise convex approximation and penalty function algorithm are used to iterate the joint beamforming and transmission power optimization model after three conversions to obtain the first iteration sensing transmit beamforming matrix. and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ;

[0111] Step 502: Transform the first iteration's sensing transmit beamforming matrix and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users Substituting the transformed joint beamforming and transmission power optimization model into the model, an updated joint beamforming and transmission power optimization model is obtained;

[0112] Step 503: Use the fractional programming method to process the updated joint beamforming and transmission power optimization model to obtain the first iteration of the first The receiving beamforming vector corresponding to the uplink communication user and the first iteration The transmission power of uplink communication users ;

[0113] Step 504: Transform the first iteration's sensing transmit beamforming matrix , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , the first iteration The receiving beamforming vector corresponding to the uplink communication user and the first iteration The transmission power of uplink communication users Substitute the objective function in step 3 to obtain the sum of the achievable rates of all communicating users in the first iteration;

[0114] Step 505: According to the method of step 501 to step 504, based on 、 、 、 Execute the next iteration and repeat it many times to get the The sum of the achievable rates of all communicating users in iteration Hedi The sum of the achievable rates of all communicating users in iteration ;in, is a positive integer;

[0115] Step 506: If , then The perceptual transmit beamforming matrix of the iteration , No. The first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , No. The first iteration The receiving beamforming vector corresponding to the uplink communication user Hedi The first iteration The transmission power of uplink communication users As the optimized value, otherwise, perform the next iteration according to the method from step 501 to step 504 until the optimized value is obtained; wherein, is a positive integer, Indicates the judgment end value.

[0116] In this embodiment, the specific process of step 501 is as follows:

[0117] Step 5011: Initialize the sensing transmit beamforming matrix to , initialize the The communication transmission beamforming matrix corresponding to the downlink communication users is: , initialize the The receiving beamforming vector corresponding to the uplink communication user is and initialize the The transmission power of an uplink communication user is ; and get the initialized sensing transmit beamforming covariance matrix and the initialization The first intermediate quantity corresponding to downlink communication users ; Wherein, in step 403 Take zero, in initialization and At first-order Taylor expansion;

[0118] Step 5012: Use the computer to set the first auxiliary amount ,and , the second auxiliary amount ,and , then the matrix The eigenvector corresponding to the maximum eigenvalue of is the first optimal value of the joint active and passive sensing beam receiving matrix ;

[0119] Step 5013: The first optimal value of the combined active and passive sensing beam receiving matrix , initialize the The receiving beamforming vector corresponding to the uplink communication user is and initialize the The transmission power of an uplink communication user is Next, set the initial penalty factor , using the stepwise convex approximation and penalty function algorithm, the first iteration of the joint beamforming and transmission power optimization model after three conversions is performed to obtain the first intermediate iteration of the sensing transmit beamforming matrix and the first intermediate iteration The communication transmission beamforming matrix corresponding to the downlink communication users ;

[0120] Step 5014: The communication transmission beamforming matrix corresponding to the downlink communication users Substitute and judge Is it true? If not, let , execute step 5015; if it is established, the sensing transmit beamforming matrix of the first intermediate iteration is and the first intermediate iteration The communication transmission beamforming matrix corresponding to the downlink communication users As the sensing transmit beamforming matrix of the first iteration and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ;in, Indicates the first intermediate iterative update value, represents the update coefficient; Indicates the maximum termination value;

[0121] Step 5015: Update the value of the first intermediate iteration Input, follow the method of step 5013 and step 5014 to perform the next iteration until Establish the corresponding sensing transmit beamforming matrix of the g-th intermediate iteration and the g-th intermediate iteration The communication transmission beamforming matrix corresponding to the downlink communication users As the sensing transmit beamforming matrix of the first iteration and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ; Wherein, g is a positive integer greater than 1.

[0122] In this embodiment, the specific process of step 502 is as follows:

[0123] The first iteration of the sensing transmit beamforming matrix Assign to , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users Assign to , substitute into the transformed joint beamforming and transmission power optimization model, and combine with the third constraint of the joint beamforming and transmission power optimization model in step 3 to obtain the updated joint beamforming and transmission power optimization model as follows:

[0124] ;

[0125] Step 503, the specific process is as follows:

[0126] Step 5031: Use a computer to convert the target function using Lagrange dual transformation The objective function is ;in, Represents the introduced auxiliary variable one, Represents the introduced auxiliary variable 2;

[0127] Step 5032: Use a computer to convert objective function 1 into objective function 2 using the quadratic conversion of fractional programming. ;in, Represents the introduction of auxiliary variable three, Represents the introduction of auxiliary variable four, represents the real part, indicates conjugation; represents the square of the modulus;

[0128] Step 5033: Use a computer to set the first introduced variable , , the second variable is introduced , ;

[0129] Step 5034, initialize the The receiving beamforming vector corresponding to the uplink communication user is and initialize the The transmission power of an uplink communication user is Next, we use a computer to make the objective function about If the derivative is 0, we get the first iteration The first optimization value of downlink communication users and the first iteration The first optimization value of uplink communication users ;in, , ;

[0130] Step 5035: Use a computer to make the objective function 2 about If the derivative is 0, we get the first iteration The second optimal value of downlink communication users and the first iteration The second optimal value of uplink communication users ;in, , ;

[0131] Step 5036, initialize the The transmission power of uplink communication users and the first iteration The first optimization value of downlink communication users , the first iteration The first optimization value of uplink communication users The first iteration The second optimal value of downlink communication users and the first iteration The second optimal value of uplink communication users Assign to 、 、 、 , and substitute it into the objective function 2 and simplify it to get the objective function 3 ;in, represents the introduction of auxiliary variable five, and , Represents the introduction of auxiliary variable six, , represents the first residual constant term;

[0132] Step 5037: Use a computer to calculate the objective function The derivative of the derivative is equal to 0, so we get ; The first iteration The receive beamforming vector corresponding to the uplink communication user;

[0133] Step 5038: The receiving beamforming vector corresponding to the uplink communication user , the first iteration The first optimization value of uplink communication users , the first iteration The first optimization value of downlink communication users , the first iteration The second optimal value of downlink communication users and the first iteration The second optimal value of uplink communication users Substitute into objective function 2 and simplify to get objective function 4 ;in, represents the introduced auxiliary variable seven, and , represents the auxiliary variable eight introduced, and , represents the second residual constant term;

[0134] Step 5039: Use a computer to convert the updated joint beamforming and transmission power optimization model into a convex problem model as follows:

[0135] ;in, represents the auxiliary variable nine introduced, and , represents the auxiliary variable introduced, and ;

[0136] Step 503A: Use a computer to solve the convex problem model using the CVX toolbox to obtain the first iteration. The transmission power of uplink communication users .

[0137] In this embodiment, the specific process of step 505 is as follows:

[0138] Step 5051: Based on the first iteration of the sensing transmit beamforming matrix , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , the first iteration The receiving beamforming vector corresponding to the uplink communication user and the first iteration The transmission power of uplink communication users , Take 1, and perform the next iteration according to steps 501 to 503A, repeat multiple times, and get the The perceptual transmit beamforming matrix of the first iteration, The first iteration The communication transmission beamforming matrix corresponding to the downlink communication user, The receiving beamforming vector corresponding to the uplink communication user of the iteration The first iteration The transmission power of each uplink communication user;

[0139] Step 5052: Based on The perceptual transmit beamforming matrix of the first iteration, The first iteration The communication transmission beamforming matrix corresponding to the downlink communication user, The receiving beamforming vector corresponding to the uplink communication user of the iteration The first iteration The transmission power of the uplink communication user is obtained by The sum of the achievable rates of all communicating users in iteration .

[0140] In this embodiment, in actual use, the area of ​​300m×300m contains 1 full-duplex communication sensing integrated primary base station, 3 secondary base stations, 1 target, 2 downlink communication users, and 2 uplink communication users. ;

[0141] The main base station is equipped with 6 transmitting antennas and 6 receiving antennas. , each secondary base station is equipped with 6 receiving antennas, then The transmitting antenna and receiving antenna are uniform linear arrays.

[0142] In this embodiment, in actual use, the target is set at the center of the area, and the communication users and secondary base stations are randomly distributed.

[0143] In this embodiment, when actually used, Taking zero to represent the initialized perceptual transmit beamforming covariance matrix and the initialization The first intermediate quantity corresponding to downlink communication users ;

[0144] Taking 1 means the first iteration The first intermediate quantity corresponding to downlink communication users and the first iteration of the perceptual transmit beamforming covariance matrix , and the sensing transmit beamforming matrix of the first iteration , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users Get.

[0145] In this embodiment, Noise variance at downlink communication users The value is -80dB, and The noise variance at each downlink communication user is the same.

[0146] No. The noise variance of uplink communication users at the base station The value is -80dB, and The noise variance of each uplink communication user at the base station is the same.

[0147] In this embodiment, the perceived noise The value is -80dB.

[0148] In this embodiment, The receiving beamforming vector corresponding to the uplink communication user The dimension is , No. Uplink communication channels for uplink communication users The dimension is , combined sensing channel The dimension is .

[0149] In this embodiment, the power of the remaining self-interference of the primary base station is is -110dB. The dimension is , combined self-interference channel The dimension is , Joint active and passive sensing beam receiving matrix The dimension is , combined beamforming matrix The dimension is .

[0150] In this embodiment, the predefined joint active and passive sensing signal-to-interference-noise ratio threshold The value is 10dB, the total power budget of the main base station is 30dBm. Maximum power budget for uplink communication users It is 16dBm.

[0151] In this embodiment, the sensing transmit beamforming matrix is ​​initialized , initialize the The communication transmission beamforming matrix corresponding to the downlink communication users Are all Gaussian matrices, initialize the The receiving beamforming vector corresponding to the uplink communication user is a Gaussian vector, initialize the The transmission power of uplink communication users A random value between 0 and 16dBm.

[0152] In this embodiment, the penalty factor is initialized for Update coefficient ; Maximum termination value The value is .

[0153] In this embodiment, the termination value is determined The value is .

[0154] In summary, the method of the present invention has simple steps and a reasonable design. While ensuring perception requirements and power, it maximizes the total rate of downlink and uplink transmission by optimizing the communication and perception transmit beamforming matrix, the receive beamforming vector corresponding to the uplink communication user, and the power allocation budget, thereby facilitating the auxiliary optimization of the communication process.

[0155] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for multi-base station cooperative ISAC combining active and passive sensing, characterized in that: The method comprises the following steps: Step 1: Build multi-base station collaborative ISAC: The multi-base station cooperation ISAC includes a primary base station, a secondary base station, a target and a communication user. The number of the secondary base stations is The primary base station and the secondary base station simultaneously detect the target and are responsible for uplink and downlink communications of communication users, wherein the communication users include multiple downlink communication users and multiple uplink communication users; wherein the primary base station is equipped with transmit antennas and receiving antennas, each secondary base station is equipped with receiving antennas, the downlink communication users and uplink communication users are both equipped with single antennas, and the number of downlink communication users is , the number of uplink communication users is , 、 、 、 、 and All are integers; the primary base station actively senses the target's echo signal, and the secondary base station passively senses the target's reflected signal; Step 2: Obtain the achievable rate of downlink communication users and uplink communication users and the joint active and passive sensing signal to interference and noise ratio; The achievable rate for downlink communication users is , No. The achievable rate for an uplink communication user is , the signal-to-interference-noise ratio of the combined active and passive sensing is , , ; Step 3: Build a joint beamforming and transmission power optimization model: A computer is used to maximize the sum of the achievable rates of all communication users, with the goal of ensuring the sensing service requirements and the power budget of the primary base station and uplink communication users as constraints. A joint beamforming and transmission power optimization model is constructed as follows: ;in, represents the joint active and passive sensing beam receiving matrix, Indicates the The communication transmission beamforming matrix corresponding to the downlink communication users is: represents the sensing transmit beamforming covariance matrix, and , represents the sensing transmit beamforming matrix, Indicates the The receiving beamforming vector corresponding to the uplink communication user, Indicates the The transmission power of an uplink communication user, represents the predefined joint active and passive sensing signal-to-interference-noise ratio threshold, represents the trace of the matrix, represents the total power budget of the master base station, Indicates the Maximum power budget for uplink communication users; It indicates universal quantifier; Indicates constraints; Indicates the maximum value; Step 4: transform the joint beamforming and transmission power optimization model three times to obtain a three-transformed joint beamforming and transmission power optimization model; Step 5: Use stepwise convex approximation, penalty function algorithm and fractional programming method to iteratively obtain the optimized value of the joint beamforming and transmission power optimization model after three conversions.

2. The method for multi-base station cooperative ISAC with combined active and passive sensing according to claim 1, characterized in that: Step 2: The specific process is as follows: Step 201: , get the SNR of downlink communication users ;in, Indicates the main base station to the Communication channels for downlink communication users, represents the conjugate transpose, Indicates the The communication transmission beamforming matrix corresponding to the downlink communication users is: The value is ,and , Indicates the Uplink communication user to the Downlink communication user channels, Indicates the Noise variance at each downlink communication user; represents the square of the modulus; DL represents the downlink communication link; Step 202: , get the The signal-to-noise ratio of uplink communication users ; Among them, UL represents the uplink communication link, Indicates the The receiving beamforming vector corresponding to the uplink communication user, , Indicates the The receiving beamforming vector corresponding to the uplink communication user to the main base station, Indicates the Uplink communication user to the The receive beamforming vector corresponding to each secondary base station is: is a positive integer, and , represents transpose, Indicates the Uplink communication channels for uplink communication users, , Indicates the Communication channels from uplink communication users to the main base station, Indicates the Uplink communication user to the Communication channels of secondary base stations, Indicates the The transmission power of an uplink communication user, Indicates the Uplink communication channels for uplink communication users, and , represents the composite interference channel, and , represents the combined sensing channel, , represents the active sensing channel, Indicates the passive sensing channels, represents the combined self-interference channel, , represents the residual self-interference channel, express Middle Rank Elements of the column, , Indicates the remaining self-interference power of the primary base station, represents a natural constant, represents the imaginary unit, represents pi, Indicates the primary base station The transmitting antenna and The distance between the receiving antennas, , , Indicates wavelength; Indicates the zero matrices, and The dimension is ; represents the square of the F norm, represents the square of the 2-norm, Indicates the The noise variance of uplink communication users at the base station is, represents the combined beamforming matrix; Step 203: According to , get the The achievable rate for downlink communication users ; according to , get the The achievable rate for uplink communication users ; Step 204: , and obtain the signal-to-interference-noise ratio of the joint active and passive sensing ;in, represents the joint active and passive sensing beam receiving matrix, and , Indicates that the master base station actively senses the receiving beamforming matrix, Indicates the The passive sensing receive beamforming matrix of the secondary base stations, , represents the corresponding beamforming matrix for communication, represents the perceived noise variance; Represents the identity matrix.

3. The method for multi-base station cooperative ISAC with combined active and passive sensing according to claim 2, characterized in that: Step 4: The specific process is as follows: Step 401: Use computer to set The intermediate variable corresponding to the downlink communication user is ,and ;in, Indicates the The first intermediate quantity corresponding to downlink communication users, and , , represents the rank of the matrix, Every element in is greater than or equal to zero; Indicates the The second intermediate quantity corresponding to downlink communication users, and , Indicates the The first intermediate quantity corresponding to downlink communication users, and , , Every element in is greater than or equal to zero; Set The intermediate variable corresponding to the uplink communication user is ,and , Indicates the The first intermediate quantity corresponding to the uplink communication users, and ; Set the third intermediate variable , ;in, represents the perceived noise variance; Step 402: Combine active and passive sensing signal to interference and noise ratio In step 401, the intermediate variables are substituted into the joint beamforming and transmission power optimization model in step 3 to perform a conversion, thereby obtaining the converted joint beamforming and transmission power optimization model as follows: ;in, Every element in is greater than or equal to zero, Satisfy the Hermitian matrix; Step 403: Use computer to Simplify and get , let the fourth intermediate variable , and for the fourth intermediate variable In the The first iteration The first intermediate quantity corresponding to downlink communication users Hedi Iterative perceptual transmit beamforming covariance matrix Perform a first-order Taylor expansion at ,and ,but ; right Simplify and get , let the fifth intermediate variable , and the fifth intermediate variable is The first iteration The first intermediate quantity corresponding to downlink communication users Hedi Iterative perceptual transmit beamforming covariance matrix Perform a first-order Taylor expansion at the position to obtain the second first-order Taylor expansion ,and ,but ;in, represents the sixth intermediate variable, and ; The seventh intermediate variable, and ; represents the eighth intermediate variable, and ; represents the ninth intermediate variable, and ;in, is a natural number; Step 404: Use computer to set The intermediate variables corresponding to downlink communication users Convert to The lower limit intermediate variable corresponding to the downlink communication user ,and ; Set The intermediate variables corresponding to the uplink communication users Convert to The lower limit intermediate variable corresponding to the uplink communication user ,and ; Step 405: Use a computer to perform a second conversion on the joint beamforming and transmission power optimization model after the first conversion to obtain a second converted joint beamforming and transmission power optimization model as follows: ； Step 406: Use a computer to constrain the non-convex rank-one Equivalent conversion to , and convert the optimization objective into ;in, represents the nuclear norm, represents the 2-norm, is the penalty factor, and ; Step 407: Use a computer to use a stepwise convex approximation method to The first iteration The first intermediate quantity corresponding to downlink communication users At, right Perform a first-order Taylor expansion to obtain the third first-order Taylor expansion ,and ; represent The eigenvector corresponding to the maximum eigenvalue of ; Step 408: Using a computer, perform a third conversion on the twice-converted joint beamforming and transmission power optimization model to obtain the three-converted joint beamforming and transmission power optimization model, as follows: 。 4. The method for multi-base station cooperative ISAC with combined active and passive sensing according to claim 3, characterized in that: Step 5: The specific process is as follows: Step 501: Initialize the sensing transmit beamforming matrix, The communication transmission beamforming matrix corresponding to the downlink communication user, the receiving beamforming vector corresponding to the uplink communication user and the After obtaining the first optimal value of the joint active and passive sensing beam receiving matrix, the stepwise convex approximation and penalty function algorithm are used to iterate the joint beamforming and transmission power optimization model after three conversions to obtain the first iteration sensing transmit beamforming matrix. and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ; Step 502: Transform the first iteration's sensing transmit beamforming matrix and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users Substituting the transformed joint beamforming and transmission power optimization model into the model, an updated joint beamforming and transmission power optimization model is obtained; Step 503: Use the fractional programming method to process the updated joint beamforming and transmission power optimization model to obtain the first iteration of the first The receiving beamforming vector corresponding to the uplink communication user and the first iteration The transmission power of uplink communication users ; Step 504: Transform the first iteration's sensing transmit beamforming matrix , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , the first iteration The receiving beamforming vector corresponding to the uplink communication user and the first iteration The transmission power of uplink communication users Substitute the objective function in step 3 to obtain the sum of the achievable rates of all communication users in the first iteration; Step 505: According to the method of step 501 to step 504, based on 、 、 、 Execute the next iteration and repeat it many times to get the The sum of the achievable rates of all communicating users in iteration Hedi The sum of the achievable rates of all communicating users in iteration ;in, is a positive integer; Step 506: If , then The perceptual transmit beamforming matrix of the iteration , No. The first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , No. The first iteration The receiving beamforming vector corresponding to the uplink communication user Hedi The first iteration The transmission power of uplink communication users As the optimized value, otherwise, perform the next iteration according to the method from step 501 to step 504 until the optimized value is obtained; wherein, is a positive integer, Indicates the judgment termination value.

5. The method for multi-base station cooperative ISAC with combined active and passive sensing according to claim 4, characterized in that: Step 501, the specific process is as follows: Step 5011: Initialize the sensing transmit beamforming matrix to , initialize the The communication transmission beamforming matrix corresponding to the downlink communication users is: , initialize the The receiving beamforming vector corresponding to the uplink communication user is and initialize the The transmission power of an uplink communication user is ; and get the initialized sensing transmit beamforming covariance matrix and the initialization The first intermediate quantity corresponding to downlink communication users ; Wherein, in step 403 Take zero, in initialization and At first-order Taylor expansion; Step 5012: Use the computer to set the first auxiliary amount ,and , the second auxiliary amount ,and , then the matrix The eigenvector corresponding to the maximum eigenvalue of is the first optimal value of the joint active and passive sensing beam receiving matrix ; Step 5013: The first optimal value of the combined active and passive sensing beam receiving matrix , initialize the The receiving beamforming vector corresponding to the uplink communication user is and initialize the The transmission power of an uplink communication user is Next, set the initial penalty factor , using the stepwise convex approximation and penalty function algorithm, the first iteration of the joint beamforming and transmission power optimization model after three conversions is performed to obtain the first intermediate iteration of the sensing transmit beamforming matrix and the first intermediate iteration The communication transmission beamforming matrix corresponding to the downlink communication users ; Step 5014: The communication transmission beamforming matrix corresponding to the downlink communication users Substitute and judge Is it true? If not, let , execute step 5015; if it is established, the sensing transmit beamforming matrix of the first intermediate iteration is and the first intermediate iteration The communication transmission beamforming matrix corresponding to the downlink communication users As the sensing transmit beamforming matrix of the first iteration and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ;in, Indicates the first intermediate iterative update value, represents the update coefficient; Indicates the maximum termination value; Step 5015: Update the value of the first intermediate iteration Input, follow the method of step 5013 and step 5014 to perform the next iteration until Establish the corresponding sensing transmit beamforming matrix of the g-th intermediate iteration and the g-th intermediate iteration The communication transmission beamforming matrix corresponding to the downlink communication users As the sensing transmit beamforming matrix of the first iteration and the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users ; Wherein, g is a positive integer greater than 1.

6. The method for multi-base station cooperative ISAC with combined active and passive sensing according to claim 5, characterized in that: Step 502, the specific process is as follows: The first iteration of the sensing transmit beamforming matrix Assign to , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users Assign to , substitute into the transformed joint beamforming and transmission power optimization model, and combine with the third constraint of the joint beamforming and transmission power optimization model in step 3 to obtain the updated joint beamforming and transmission power optimization model as follows: ； Step 503, the specific process is as follows: Step 5031: Use a computer to convert the target function using Lagrange dual transformation The objective function is ;in, Represents the introduced auxiliary variable one, Represents the introduced auxiliary variable 2; Step 5032: Use a computer to convert objective function 1 into objective function 2 using the quadratic conversion of fractional programming. ;in, Represents the introduction of auxiliary variable three, Represents the introduction of auxiliary variable four, represents the real part, indicates conjugation; represents the square of the modulus; Step 5033: Use a computer to set the first introduced variable , , the second variable is introduced , ; Step 5034, initialize the The receiving beamforming vector corresponding to the uplink communication user is and initialize the The transmission power of an uplink communication user is Next, we use a computer to make the objective function about If the derivative is 0, we get the first iteration The first optimization value of downlink communication users and the first iteration The first optimization value of uplink communication users ;in, , ; Step 5035: Use a computer to make the objective function 2 about If the derivative is 0, we get the first iteration The second optimal value of downlink communication users and the first iteration The second optimal value of uplink communication users ;in, , ; Step 5036, initialize the The transmission power of uplink communication users and the first iteration The first optimization value of downlink communication users , the first iteration The first optimization value of uplink communication users The first iteration The second optimal value of downlink communication users and the first iteration The second optimal value of uplink communication users Assign to 、 、 、 , and substitute it into the objective function 2 and simplify it to get the objective function 3 ;in, represents the introduction of auxiliary variable five, and , Represents the introduction of auxiliary variable six, , represents the first residual constant term; Step 5037: Use a computer to calculate the objective function The derivative of the derivative is equal to 0, so we get ; The first iteration The receive beamforming vector corresponding to the uplink communication user; Step 5038: The receiving beamforming vector corresponding to the uplink communication user , the first iteration The first optimization value of uplink communication users , the first iteration The first optimization value of downlink communication users , the first iteration The second optimal value of downlink communication users and the first iteration The second optimal value of uplink communication users Substitute into objective function 2 and simplify to get objective function 4 ;in, represents the introduced auxiliary variable seven, and , represents the auxiliary variable eight introduced, and , represents the second residual constant term; Step 5039: Use a computer to convert the updated joint beamforming and transmission power optimization model into a convex problem model as follows: ;in, represents the auxiliary variable nine introduced, and , represents the auxiliary variable introduced, and ; Step 503A: Use a computer to solve the convex problem model using the CVX toolbox to obtain the first iteration. The transmission power of uplink communication users .

7. The method for multi-base station cooperative ISAC with combined active and passive sensing according to claim 6, characterized in that: Step 505, the specific process is: Step 5051: Based on the first iteration of the sensing transmit beamforming matrix , the first iteration The communication transmission beamforming matrix corresponding to the downlink communication users , the first iteration The receiving beamforming vector corresponding to the uplink communication user and the first iteration The transmission power of uplink communication users , Take 1, and perform the next iteration according to steps 501 to 503A, repeat multiple times, and get the The perceptual transmit beamforming matrix of the first iteration, The first iteration The communication transmission beamforming matrix corresponding to the downlink communication user, The receiving beamforming vector corresponding to the uplink communication user of the iteration The first iteration The transmission power of each uplink communication user; Step 5052: Based on The perceptual transmit beamforming matrix of the first iteration, The first iteration The communication transmission beamforming matrix corresponding to the downlink communication user, The receiving beamforming vector corresponding to the uplink communication user of the iteration The first iteration The transmission power of the uplink communication user is obtained by The sum of the achievable rates of all communicating users in iteration .

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