Multi-base-station cooperation ISAC method combining active and passive sensing

Abstract

The invention discloses a multi-base-station cooperative ISAC method combining active and passive perception. The method comprises the following steps: 1, constructing a multi-base-station cooperative ISAC; 2, obtaining the reachable rates of a downlink communication user and an uplink communication user and a combined active and passive sensing signal to interference plus noise ratio; 3, constructing a joint beam forming and transmission power optimization model; 4, performing three-time conversion on the joint beam forming and transmission power optimization model to obtain a joint beam forming and transmission power optimization model after three-time conversion; and 5, iterating the joint beam forming and transmission power optimization model after the three times of conversion by adopting a step-by-step convex approximation, a penalty function algorithm and a fractional programming method to obtain an optimization value. According to the method disclosed by the invention, while the sensing requirement and power are ensured, the sum rate of downlink and uplink transmission is maximized by optimizing a communication and sensing transmission beam forming matrix, a receiving beam forming vector corresponding to an uplink communication user and a power distribution budget, and the optimization of a communication process is assisted.

Description

Technical Field

[0001] The present invention belongs to the technical field of UAV ISAC, and in particular relates to a multi-base station cooperative ISAC method that combines active and passive sensing. Background Art

[0002] Integrated Sensing and Communication (ISAC) refers to a technology where sensing and communication share the same frequency band and hardware. This technology is considered one of the important candidate technologies for next-generation mobile communications. Multi-base station cooperative ISAC has emerged as a new technology, providing a potential approach for integrated sensing and communication technology. From the communication perspective, multi-base station cooperative ISAC can effectively ensure high-quality communication through joint signal transmission. From the sensing perspective, multi-base station cooperative ISAC can achieve multi-angle sensing, providing rich sensing information, improving sensing accuracy, and expanding the sensing range.

[0003] In the active sensing mode, there is self-interference between the transmitting antenna and the receiving antenna in a full-duplex transmitting and receiving base station. In the passive sensing mode, there is a problem of time asynchrony between the transmitting base station and the receiving base station. These greatly degrade the system performance. By combining active and passive sensing, the self-interference of the full-duplex primary base station and the time asynchrony problem between the transmitting base station and the receiving base station can be effectively suppressed, and at the same time, the sensing accuracy and reliability can be enhanced.

[0004] Therefore, there is a need for a multi-base station cooperative ISAC method that combines active and passive sensing. While ensuring sensing requirements and power, by optimizing the communication and sensing transmit beamforming matrices, the receive beamforming vectors corresponding to the uplink communication users, and the power allocation budget, the sum rate of downlink and uplink transmissions can be maximized, 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 provide a multi-base station cooperative ISAC method that combines active and passive sensing in view of the above-mentioned deficiencies in the prior art. The method steps are simple and reasonably designed. While ensuring sensing requirements and power, by optimizing the communication and sensing transmit beamforming matrices, the receive beamforming vectors corresponding to the uplink communication users, and the power allocation budget, the sum rate of downlink and uplink transmissions can be maximized, thereby facilitating the auxiliary optimization of the communication process.

[0006] To solve the above technical problem, the technical solution adopted by the present invention is: a multi-base station cooperative ISAC method that combines active and passive sensing, the method comprising the following steps: Step 1. Construct a multi-base station cooperative ISAC: The multi-base station cooperative ISAC includes a primary base station, secondary base stations, a target, and communication users. The number of secondary base stations is There are [number] of them. The primary base station and the secondary base station simultaneously detect the target and are responsible for the uplink and downlink communications of the communication users. The communication users include multiple downlink communication users and multiple uplink communication users. Among them, the primary base station is equipped with transmitting antennas and receiving antennas. Each secondary base station is equipped with receiving antennas. The downlink communication users and the uplink communication users are both equipped with single antennas. The number of downlink communication users is , and the number of uplink communication users is , , , , , and are all integers. The primary base station actively senses the echo signal of the target, and the secondary base station passively senses the reflected signal of the target; Step 2: Obtain the achievable rates of the downlink communication users and the uplink communication users and the combined active and passive sensing signal-to-interference-plus-noise ratio (SINR); among them, the achievable rate of the th downlink communication user is , the achievable rate of the th uplink communication user is , and the combined active and passive sensing SINR is , , ; Step 3: Construct a joint beamforming and transmission power optimization model: Using a computer, with the goal of maximizing the sum of the achievable rates of all communication users and subject to the constraints of ensuring the sensing service requirements and the power budgets of the primary base station and the uplink communication users, a joint beamforming and transmission power optimization model is constructed as follows: ; where represents the combined active and passive sensing beam reception matrix, represents the communication transmission beamforming matrix corresponding to the th downlink communication user, represents the sensing transmission beamforming covariance matrix, and , represents the sensing transmission beamforming matrix, represents the receiving beamforming vector corresponding to the th uplink communication user, represents the transmission power of the th uplink communication user, represents the predefined combined active and passive sensing SINR threshold, represents the trace of the matrix, represents the total power budget of the primary base station, Represents the maximum power budget of the th uplink communication user; Represents the universal quantifier; Represents the constraint condition; Represents the maximum value; Step 4: Perform three conversions on the joint beamforming and transmission power optimization model to obtain the joint beamforming and transmission power optimization model after three conversions; Step 5: Use the successive convex approximation, penalty function algorithm, and fractional programming method to iteratively obtain the optimal value for the joint beamforming and transmission power optimization model after three conversions.

[0007] Furthermore, for Step 2, the specific process is as follows: Step 201: According to , obtain the signal-to-noise ratio of the th downlink communication user; where represents the communication channel from the main base station to the th downlink communication user, represents the conjugate transpose, represents the communication transmit beamforming matrix corresponding to the th downlink communication user, takes a value of , and , represents the channel from the th uplink communication user to the th downlink communication user, represents the noise variance at the th downlink communication user; represents the square of the modulus; DL represents the downlink communication link; Step 202: According to , obtain the signal-to-noise ratio of the th uplink communication user; where UL represents the uplink communication link, represents the receive beamforming vector corresponding to the th uplink communication user, , represents the receive beamforming vector corresponding to the th uplink communication user to the main base station, represents the receive beamforming vector corresponding to the th uplink communication user to the th secondary base station, is a positive integer, and , represents the transpose, represents the The uplink communication channel of an uplink communication user, , denotes the communication channel from the -th uplink communication user to the main base station, denotes the communication channel from the -th uplink communication user to the -th secondary base station, denotes the transmit power of the -th uplink communication user, denotes the uplink communication channel of the -th uplink communication user, and , denotes the composite interference channel, and , denotes the combined sensing channel, , denotes the active sensing channel, denotes the -th passive sensing channel, denotes the combined self-interference channel, , denotes the residual self-interference channel, denotes the element in the -th row and -th column of , denotes the power of the residual self-interference of the main base station, denotes the natural constant, denotes the imaginary unit, denotes the pi, denotes the distance between the -th transmit antenna and the -th receive antenna in the main base station, , , denotes the wavelength; denotes the -th zero matrix, and its dimension is ; denotes the square of the F norm, denotes the square of the 2 norm, denotes the noise variance of the -th uplink communication user at the base station, denotes the combined beamforming matrix; Step 203, according to , obtain the achievable rate of the -th downlink communication user According to , obtain the achievable rate of the th uplink communication user ; Step 204, according to , obtain the combined active and passive sensing signal-to-interference-plus-noise ratio ; where represents the combined active and passive sensing beam reception matrix, and , represents the active sensing reception beamforming matrix of the primary base station, represents the th passive sensing reception beamforming matrix of the secondary base station, , represents the corresponding beamforming matrix of communication, represents the noise variance of sensing; represents the identity matrix.

[0008] Furthermore, step four, the specific process is as follows: Step 401, use a computer to set the intermediate variable corresponding to the th downlink communication user as , and ; where represents the first intermediate quantity corresponding to the th downlink communication user, and , , represents the rank of the matrix, each element in is greater than or equal to zero; represents the second intermediate quantity corresponding to the th downlink communication user, and , represents the th first intermediate quantity corresponding to the downlink communication user, and , , each element in is greater than or equal to zero; Set the intermediate variable corresponding to the th uplink communication user as , and , represents the th first intermediate quantity corresponding to the uplink communication user, and ; Set the third intermediate variable , ; where represents the sensing noise; Step 402, take the combined active and passive sensing signal-to-interference-plus-noise ratio , the intermediate variable in step 401 is substituted into the joint beamforming and transmission power optimization model in step 3 for one transformation to obtain the joint beamforming and transmission power optimization model after the first transformation as follows: ; where each element in is greater than or equal to zero, satisfies the Hermitian matrix; Step 403, use a computer to Simplify to obtain , let the fourth intermediate variable , and perform a first-order Taylor expansion on the fourth intermediate variable at the first intermediate quantity corresponding to the th downlink communication user in the th iteration and the sensing transmit beamforming covariance matrix at the th iteration to obtain the first first-order Taylor expansion formula , and , then ; For Simplify to obtain , let the fifth intermediate variable , and perform a first-order Taylor expansion on the fifth intermediate variable at the first intermediate quantity corresponding to the th downlink communication user in the th iteration and the sensing transmit beamforming covariance matrix at the th iteration to obtain the second first-order Taylor expansion formula , and , then ; where represents the sixth intermediate variable, and ; The seventh intermediate variable, and ; represents the eighth intermediate variable, and ; represents the ninth intermediate variable, and ; where is a natural number; Step 404, use a computer to set the intermediate variable corresponding to the th downlink communication user to be converted to the lower limit intermediate variable corresponding to the th downlink communication user, and ; Set the Intermediate variables corresponding to an uplink communication user Convert to the lower bound intermediate variable corresponding to the th uplink communication user, and ; Step 405: Use a computer to perform a secondary conversion on the jointly beamformed and transmission power optimized model after the first conversion to obtain the jointly beamformed and transmission power optimized model after the secondary conversion, as follows: ; Step 406: Use a computer to equivalently convert the non-convex rank-one constraint to , and convert the optimization objective to ; where represents the nuclear norm, represents the 2-norm, is the penalty factor, and ; Step 407: Use a computer to utilize the successive convex approximation method to perform a first-order Taylor expansion of at the th first intermediate quantity corresponding to the th downlink communication user in the th iteration to obtain the third first-order Taylor expansion , and ; represents the eigenvector corresponding to the maximum eigenvalue of ; Step 408: Use a computer to perform a third conversion on the jointly beamformed and transmission power optimized model after the secondary conversion to obtain the jointly beamformed and transmission power optimized model after the third conversion, as follows: .

[0009] Furthermore, step five, the specific process is as follows: Step 501: Initialize the sensing transmit beamforming matrix, the communication transmit beamforming matrix corresponding to the th downlink communication user, the receive beamforming vector corresponding to the uplink communication user, and the transmit power of the th uplink communication user. After obtaining the first optimal value of the joint active and passive sensing beam reception matrix, use the successive convex approximation and penalty function algorithms to iterate on the jointly beamformed and transmission power optimized model after the third conversion to obtain the sensing transmit beamforming matrix in the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user in the first iteration; Step 502: Substitute the sensing transmit beamforming matrix of the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration into the jointly beamformed and transmission power optimized model after one transformation to obtain an updated jointly beamformed and transmission power optimized model; Step 503: Use the fractional programming method to process the updated jointly beamformed and transmission power optimized model to obtain the receive beamforming vector corresponding to the th uplink communication user of the first iteration and the transmit power of the th uplink communication user of the first iteration; Step 504: Substitute the sensing transmit beamforming matrix of the first iteration , the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration , the receive beamforming vector corresponding to the th uplink communication user of the first iteration and the transmit power of the th uplink communication user of the first iteration into the objective function in step three to obtain the sum of achievable rates of all communication users in the first iteration; Step 505: According to the method in steps 501 to 504, based on , , , perform the next iteration, repeat multiple times to obtain the sum of achievable rates of all communication users in the th iteration and the sum of achievable rates of all communication users in the th iteration ; where is a positive integer; Step 506: If , then the sensing transmit beamforming matrix in the th iteration , the communication transmit beamforming matrix corresponding to the th downlink communication user in the th iteration , the receive beamforming vector corresponding to the th uplink communication user in the th iteration and the transmit power of the th uplink communication user in the As the optimized value, otherwise, perform the next iteration according to the methods in steps 501 to 504 until the optimized value is obtained; where is a positive integer, represents the judgment termination value.

[0010] Furthermore, for step 501, the specific process is as follows: Step 5011: Initialize the sensing transmit beamforming matrix as , initialize the communication transmit beamforming matrix corresponding to the th downlink communication user as , initialize the receive beamforming vector corresponding to the th uplink communication user as and initialize the transmit power of the th uplink communication user as ; and obtain the initialized sensing transmit beamforming covariance matrix and the first intermediate quantity corresponding to the th downlink communication user; where in step 403 takes zero, and performs a first-order Taylor expansion at the initialization of and ; Step 5012: Use the computer to set the first auxiliary quantity , and , the second auxiliary quantity , and , then the eigenvector corresponding to the maximum eigenvalue of the matrix is the first optimal value of the joint active and passive sensing beam receiving matrix; Step 5013: At the first optimal value of the joint active and passive sensing beam receiving matrix, the receive beamforming vector corresponding to the th uplink communication user initialized, and the transmit power of the th uplink communication user initialized, set the initial penalty factor , and use the successive convex approximation and penalty function algorithm to perform the first iteration on the jointly beamformed and transmission power optimization model after three conversions, and obtain the sensing transmit beamforming matrix of the first intermediate iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user of the first intermediate iteration; Step 5014: Take the Communication transmit beamforming matrix corresponding to a downlink communication user Substitute and judge Whether it holds. If it does not hold, let , and execute step 5015; if it holds, then use the sensing transmit beamforming matrix of the first intermediate iteration And the first intermediate iteration's Communication transmit beamforming matrix corresponding to the downlink communication user Respectively as the sensing transmit beamforming matrix of the first iteration And the first iteration's Communication transmit beamforming matrix corresponding to the downlink communication user ; where Represents the update value of the first intermediate iteration, Represents the update coefficient; Represents the maximum termination value; Step 5015: Input the update value Of the first intermediate iteration, and perform the next iteration according to the methods of step 5013 and step 5014 until The sensing transmit beamforming matrix of the g-th intermediate iteration corresponding to the established And the g-th intermediate iteration's Communication transmit beamforming matrix corresponding to the downlink communication user Respectively as the sensing transmit beamforming matrix of the first iteration And the first iteration's Communication transmit beamforming matrix corresponding to the downlink communication user ; where g is a positive integer greater than 1.

[0011] Furthermore, step 502 is as follows: Assign the sensing transmit beamforming matrix of the first iteration To , and assign the communication transmit beamforming matrix corresponding to the Th downlink communication user of the first iteration To , substitute into the jointly converted beamforming and transmission power optimization model after one conversion, and combine with the third constraint condition of the jointly converted beamforming and transmission power optimization model in step three to obtain the updated jointly converted beamforming and transmission power optimization model as follows: ; Step 503 is as follows: Step 5031: Use a computer to equivalently transform the objective function Into objective function one ; among which, represents the introduced auxiliary variable one, represents the introduced auxiliary variable two; Step 5032: Use a computer to convert the first objective function into the second objective function by means of the quadratic transformation of fractional programming ; among which, represents the introduced auxiliary variable three, represents the introduced auxiliary variable four, represents taking the real part, represents conjugation; represents the square of the modulus; Step 5033: Use a computer to set the first introduced variable , , the second introduced variable , ; Step 5034: Initialize the receive beamforming vector corresponding to the th uplink communication user as and initialize the transmit power of the th uplink communication user as Under this, use a computer to make the derivative of the first objective function with respect to equal to 0, then the first optimization value of the th downlink communication user in the first iteration and the first optimization value of the th uplink communication user in the first iteration are obtained; among which, , ; Step 5035: Use a computer to make the derivative of the second objective function with respect to equal to 0, then the second optimization value of the th downlink communication user in the first iteration and the second optimization value of the th uplink communication user in the first iteration are obtained; among which, , ; Step 5036: Initialize the transmit power of the th uplink communication user, as well as the first optimization value of the th downlink communication user in the first iteration, the first optimization value of the th uplink communication user in the first iteration, the second optimization value of the th downlink communication user in the first iteration, and the second optimization value The second optimization value of an uplink communication user is assigned to , , , , and substituted into the second objective function and simplified to obtain the third objective function ; where represents the introduced auxiliary variable five, and , represents the introduced auxiliary variable six, , represents the first remaining constant term; Step 5037: Use a computer to set the derivative of the third objective function with respect to equal to 0 to obtain ; represents the receiving beamforming vector corresponding to the th uplink communication user in the first iteration; Step 5038: Substitute the receiving beamforming vector corresponding to the th uplink communication user in the first iteration, the first optimization value of the th uplink communication user in the first iteration, the first optimization value of the th downlink communication user in the first iteration, the second optimization value of the th downlink communication user in the first iteration, and the second optimization value of the th uplink communication user in the first iteration into the second objective function and simplify to obtain the fourth objective function ; where represents the introduced auxiliary variable seven, and , represents the introduced auxiliary variable eight, and , represents the second remaining 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: ; where represents the introduced auxiliary variable nine, and , represents the introduced auxiliary variable ten, and ; Step 503A: Use a computer to solve the convex problem model using the CVX toolbox to obtain the transmission power of the th uplink communication user in the first iteration.

[0012] Further, in step 505, the specific process is as follows: Step 5051: Based on the sensing transmit beamforming matrix of the first iteration , the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration , the receive beamforming vector corresponding to the th uplink communication user of the first iteration and the transmit power of the th uplink communication user of the first iteration , take 1, and perform the next iteration according to steps 501 to 503A, repeat multiple times to obtain the sensing transmit beamforming matrix of the th iteration, the communication transmit beamforming matrix corresponding to the th iteration of the th downlink communication user, the receive beamforming vector corresponding to the uplink communication user of the th iteration, and the transmit power of the th iteration of the th uplink communication user; Step 5052: Based on the sensing transmit beamforming matrix of the th iteration, the communication transmit beamforming matrix corresponding to the th iteration of the th downlink communication user, the receive beamforming vector corresponding to the uplink communication user of the th iteration, and the transmit power of the th iteration of the th uplink communication user, obtain the sum of achievable rates of all communication users of the th iteration .

[0013] The present invention has the following advantages compared with the prior art: 1. The method steps of the present invention are simple and easy to implement. By jointly designing transmit beamforming, power allocation, and the configuration of the receive beamforming vector corresponding to uplink communication users, with the goal of maximizing the sum of uplink and downlink achievable rates, and using a joint active and passive sensing framework, the communication ability is improved while ensuring the sensing performance.

[0014] 2. The present invention adopts the method of successive convex approximation and first-order Taylor expansion, and performs three conversions on the joint beamforming and transmission power optimization model to obtain the joint beamforming and transmission power optimization model after three conversions, so that the joint beamforming and transmission power optimization model after three conversions is transformed into a convex optimization, thereby realizing subsequent iterative solution.

[0015] 3. The present invention decouples the joint beamforming and transmission power optimization model into sub-problems, reducing the complexity of variable coupling. Specifically, first, the sensing receiving vector is solved based on the generalized Rayleigh entropy. Given the receiving beamforming vector and transmission power corresponding to the uplink communication users, the relaxation technique, the sequential convex approximation method, and the penalty function algorithm are introduced to transform the joint beamforming and transmission power optimization model after three transformations into an iteratively solvable sub-problem, obtaining the sensing transmit beamforming matrix of the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration ; Then, given the sensing transmit beamforming matrix of the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration , the fractional programming method is introduced to transform the updated joint beamforming and transmission power optimization model into an iteratively solvable sub-problem, obtaining the receiving beamforming vector corresponding to the th uplink communication user of the first iteration ; Finally, given the receiving beamforming vector corresponding to the th uplink communication user of the first iteration , the convex problem model is solved to obtain the transmission power of the th uplink communication user of the first iteration. By constructing a hierarchical iterative optimization strategy, the efficient collaboration of multi-variable optimization is realized, significantly reducing the computational complexity while ensuring the convergence of the algorithm, providing a feasible path for system performance optimization

[0016] 4. The present invention can effectively suppress the self-interference of the full-duplex macro base station and the time asynchrony problem between the transmitting base station and the receiving base station through the time registration and signal joint processing between the active sensing and passive sensing signals by jointly using active and passive sensing. At the same time, it can also enhance the sensing accuracy and reliability, making the joint beamforming and transmission power optimization model more in line with the actual situation, facilitating generalization and being more practical

[0017] In summary, the method steps of the present invention are simple and reasonably designed. While ensuring the sensing requirements and power, by optimizing the communication and sensing transmit beamforming matrices, the receiving beamforming vector corresponding to the uplink communication users, and the power allocation budget, the sum rate of downlink and uplink transmissions is maximized, thus facilitating the auxiliary optimization of the communication process

[0018] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments Description of the Drawings

[0019] Figure 1 This is the flowchart of the method of the present invention. Specific implementation mode

[0020] Such as Figure 1 A method for multi-base station cooperative ISAC that combines active and passive sensing includes the following steps: Step 1: Construct a multi-base station cooperative ISAC: The multi-base station cooperative ISAC includes a primary base station, secondary base stations, a target, and communication users. The number of secondary base stations is The primary base station and the secondary base stations simultaneously detect the target and are responsible for the uplink and downlink communications of the communication users. The communication users include multiple downlink communication users and multiple uplink communication users. Among them, the primary base station is equipped with transmitting antennas and receiving antennas. Each secondary base station is equipped with receiving antennas. The downlink communication users and the uplink communication users are both equipped with single antennas. The number of downlink communication users is , and the number of uplink communication users is , , , , , and are all integers. The primary base station actively senses the echo signal of the target, and the secondary base stations passively sense the reflected signal of the target; Step 2: Obtain the achievable rates of the downlink communication users and the uplink communication users and the combined active and passive sensing signal-to-interference-plus-noise ratio; among them, the achievable rate of the th downlink communication user is , the achievable rate of the th uplink communication user is , and the combined active and passive sensing signal-to-interference-plus-noise ratio is , , ; Step 3: Construct a combined beamforming and transmission power optimization model: Using a computer, with the goal of maximizing the sum of the achievable rates of all communication users and subject to the constraints of ensuring the sensing service requirements and the power budgets of the primary base station and the uplink communication users, a combined beamforming and transmission power optimization model is constructed as follows: ; among them, represents the combined active and passive sensing beam reception matrix, represents the communication transmission beamforming matrix corresponding to the th downlink communication user, denotes the covariance matrix of the sensing transmit beamforming, and , denotes the sensing transmit beamforming matrix, denotes the th receive beamforming vector corresponding to the th uplink communication user, denotes the transmit power of the th uplink communication user, denotes the trace of the matrix, denotes the total power budget of the macro base station, denotes the th maximum power budget of the th uplink communication user; denotes the universal quantifier; denotes the maximum value; Step 4: Perform three transformations on the joint beamforming and transmission power optimization model to obtain the joint beamforming and transmission power optimization model after three transformations; Step 5: Adopt the successive convex approximation, penalty function algorithm, and fractional programming method to iteratively obtain the optimized values for the joint beamforming and transmission power optimization model after three transformations.

[0021] In this embodiment, Step 2 is specifically as follows: Step 201: According to , obtain the signal-to-noise ratio of the th downlink communication user; where denotes the communication channel from the macro base station to the th downlink communication user, denotes the conjugate transpose, denotes the th communication transmit beamforming matrix corresponding to the th downlink communication user, takes a value of , and denotes the th uplink communication user to the th downlink communication user channel, denotes the th noise variance at the th downlink communication user; Step 202: According to , obtain the signal-to-noise ratio of the th uplink communication user; where UL denotes the uplink communication link, denotes the The received beamforming vector corresponding to an uplink communication user, , denotes the received beamforming vector corresponding to the th uplink communication user to the main base station, denotes the received beamforming vector corresponding to the th uplink communication user to the th secondary base station, is a positive integer, and , denotes the transpose, denotes the th uplink communication channel of the th uplink communication user, denotes the communication channel from the th uplink communication user to the main base station, denotes the communication channel from the th uplink communication user to the th secondary base station, denotes the th uplink transmission power of the th uplink communication user, denotes the th uplink communication channel of the th uplink communication user, and denotes the composite interference channel, and , denotes the combined sensing channel, , denotes the active sensing channel, denotes the th passive sensing channel, denotes the combined self-interference channel, , denotes the residual self-interference channel, denotes the th element in the th row and th column of , denotes the power of the residual self-interference of the main base station, denotes the natural constant, denotes the imaginary unit, denotes the pi, denotes the distance between the th transmitting antenna and the th receiving antenna in the main base station, , , denotes the wavelength; denotes the th zero matrix, and the dimension is ; represents the square of the Frobenius norm, represents the square of the 2-norm, represents the noise variance of the th uplink communication user at the base station; Step 203, according to , obtain the achievable rate of the th downlink communication user; According to , obtain the achievable rate of the th uplink communication user; Step 204, according to , obtain the joint active and passive sensing signal-to-interference-plus-noise ratio ; where represents the joint active and passive sensing beam receiving matrix, and , represents the active sensing receiving beamforming matrix of the primary base station, represents the th passive sensing receiving beamforming matrix of the secondary base station, , represents the corresponding beamforming matrix of communication, represents the noise variance of sensing; represents the identity matrix.

[0022] In this embodiment, step four is specifically as follows: Step 401, use a computer to set the intermediate variable corresponding to the th downlink communication user as , and ; where represents the first intermediate quantity corresponding to the th downlink communication user, and , , represents the rank of the matrix, each element in represents the second intermediate quantity corresponding to the th downlink communication user, and , represents the th first intermediate quantity corresponding to the downlink communication user, and , , each element in Set the intermediate variable corresponding to the th uplink communication user as , and , represents the first intermediate quantity corresponding to the th uplink communication user, and ; Set the third intermediate variable , ; where represents the perceived noise; Step 402: Substitute the joint active and passive sensing signal-to-interference-plus-noise ratio and the intermediate variables in Step 401 into the joint beamforming and transmission power optimization model in Step 3 for a first transformation to obtain the joint beamforming and transmission power optimization model after the first transformation, as follows: ; where each element in is greater than or equal to zero, satisfies the Hermitian matrix; Step 403: Use a computer to simplify to obtain , let the fourth intermediate variable , and perform a first-order Taylor expansion of the fourth intermediate variable at the first intermediate quantity corresponding to the th downlink communication user in the th iteration and the perceived transmit beamforming covariance matrix in the th iteration to obtain the first first-order Taylor expansion , and , then ; Simplify to obtain , let the fifth intermediate variable , and perform a first-order Taylor expansion of the fifth intermediate variable at the first intermediate quantity corresponding to the th downlink communication user in the th iteration and the perceived transmit beamforming covariance matrix in the th iteration to obtain the second first-order Taylor expansion , and , then ; where represents the sixth intermediate variable, and ; the seventh intermediate variable, and ; represents the eighth intermediate variable, and ; represents the ninth intermediate variable, and ; wherein, is a natural number; Step 404: Use a computer to set the intermediate variable corresponding to the -th downlink communication user and convert it into the lower-bound intermediate variable corresponding to the -th downlink communication user, and ; Set the intermediate variable corresponding to the -th uplink communication user and convert it into the lower-bound intermediate variable corresponding to the -th uplink communication user, and ; Step 405: Use a computer to perform a second conversion on the jointly beamformed and transmit power optimized model after the first conversion to obtain the jointly beamformed and transmit power optimized model after the second conversion, as follows: ; Step 406: Use a computer to equivalently convert the non-convex rank-one constraint into , and convert the optimization objective into ; wherein, represents the nuclear norm, represents the 2-norm, is the penalty factor, and ; Step 407: Use a computer to perform a first-order Taylor expansion of at the -th first intermediate quantity corresponding to the -th downlink communication user in the -th iteration using the sequential convex approximation method to obtain the third first-order Taylor expansion , and represents the eigenvector corresponding to the maximum eigenvalue of ; Step 408: Use a computer to perform a third conversion on the jointly beamformed and transmit power optimized model after the second conversion to obtain the jointly beamformed and transmit power optimized model after the third conversion, as follows: .

[0023] In this embodiment, step five is specifically as follows: Step 501: Initialize the sensing transmit beamforming matrix, the communication transmit beamforming matrix corresponding to the -th downlink communication user, the receive beamforming vector corresponding to the uplink communication user, and the The transmit power of an uplink communication user. After obtaining the first optimal value of the joint active and passive sensing beam reception matrix, the successive convex approximation and penalty function algorithms are used to iterate the joint beamforming and transmission power optimization model after three conversions to obtain the sensing transmit beamforming matrix of the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration ; Step 502: Substitute the sensing transmit beamforming matrix of the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration into the joint beamforming and transmission power optimization model after one conversion to obtain the updated joint beamforming and transmission power optimization model; Step 503: Use the fractional programming method to process the updated joint beamforming and transmission power optimization model to obtain the receive beamforming vector corresponding to the th uplink communication user of the first iteration and the transmit power of the th uplink communication user of the first iteration Step 504: Substitute the sensing transmit beamforming matrix of the first iteration , the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration , the receive beamforming vector corresponding to the th uplink communication user of the first iteration and the transmit power of the th uplink communication user of the first iteration into the objective function in Step 3 to obtain the sum of achievable rates of all communication users in the first iteration; Step 505: According to the methods in Step 501 to Step 504, based on , , , perform the next iteration, repeat multiple times to obtain the sum of achievable rates of all communication users in the th iteration and the sum of achievable rates of all communication users in the th iteration ; where is a positive integer; Step 506: If , then the sensing transmit beamforming matrix of the th iteration , the The communication transmit beamforming matrix corresponding to the th downlink communication user in the -th iteration, the th receive beamforming vector corresponding to the th uplink communication user in the -th iteration, and the th transmit power of the th uplink communication user in the -th iteration are used as the optimization values; otherwise, the next iteration is performed according to the method of steps 501 to 504 until the optimization value is obtained; where is a positive integer, and represents the judgment termination value.

[0024] In this embodiment, step 501 is specifically as follows: Step 5011: Initialize the sensing transmit beamforming matrix as , initialize the communication transmit beamforming matrix corresponding to the th downlink communication user as , initialize the receive beamforming vector corresponding to the th uplink communication user as , and initialize the transmit power of the th uplink communication user as ; and obtain the initialized sensing transmit beamforming covariance matrix and the initialized first intermediate quantity corresponding to the th downlink communication user; where in step 403 takes zero, and a first-order Taylor expansion is performed at the initialization and ; Step 5012: Use a computer to set the first auxiliary quantity , and , the second auxiliary quantity , and , then the eigenvector corresponding to the maximum eigenvalue of the matrix is the first optimal value of the joint active and passive sensing beam reception matrix; Step 5013: With the first optimal value of the joint active and passive sensing beam reception matrix, the receive beamforming vector corresponding to the th uplink communication user initialized as , and the transmit power of the th uplink communication user initialized as , set the initial penalty factor , the first iteration is performed on the jointly beamformed and transmission power optimization model after three conversions using the stepwise convex approximation and penalty function algorithms to obtain the sensing transmit beamforming matrix for the first intermediate iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user in the first intermediate iteration ; Step 5014: Substitute the communication transmit beamforming matrix corresponding to the th downlink communication user in the first intermediate iteration and determine whether holds. If it does not hold, let , and execute Step 5015; if it holds, then use the sensing transmit beamforming matrix in the first intermediate iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user in the first intermediate iteration as the sensing transmit beamforming matrix in the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user in the first iteration respectively; where represents the updated value in the first intermediate iteration, and represents the update coefficient; represents the maximum termination value; Step 5015: Input the updated value in the first intermediate iteration and perform the next iteration according to the methods in Step 5013 and Step 5014 until holds, and use the sensing transmit beamforming matrix in the gth intermediate iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user in the gth intermediate iteration as the sensing transmit beamforming matrix in the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user in the first iteration respectively; where g is a positive integer greater than 1.

[0025] In this embodiment, Step 502 is specifically as follows: Assign the sensing transmit beamforming matrix in the first iteration to , and assign the communication transmit beamforming matrix corresponding to the th downlink communication user in the first iteration to , substitute it into the jointly beamforming and transmission power optimization model after the first conversion, and combine with the third constraint condition of the jointly beamforming and transmission power optimization model in step 3 to obtain the updated jointly beamforming and transmission power optimization model as follows: ; Step 503, the specific process is as follows: Step 5031. Use a computer to equivalently transform the objective function by Lagrangian dual transformation into objective function one ; where, represents the introduced auxiliary variable one, represents the introduced auxiliary variable two; Step 5032. Use a computer to transform objective function one into objective function two by quadratic transformation of fractional programming ; where, represents the introduced auxiliary variable three, represents the introduced auxiliary variable four, represents taking the real part, represents the conjugate; represents the square of the modulus; Step 5033. Use a computer to set the first introduced variable , , the second introduced variable , ; Step 5034. Initialize the receive beamforming vector corresponding to the th uplink communication user as and initialize the transmission power of the th uplink communication user as Under the condition, use a computer to make the derivative of objective function one with respect to equal to 0, then obtain the first optimization value of the th downlink communication user in the first iteration and the first optimization value of the th uplink communication user in the first iteration; where, , ; Step 5035. Use a computer to make the derivative of objective function two with respect to equal to 0, then obtain the second optimization value of the th downlink communication user in the first iteration and the second optimization value of the th uplink communication user in the first iteration; where, , ; Step 5036, initialize the transmit power of the th uplink communication user and the first optimized value of the th downlink communication user in the first iteration , the first optimized value of the th uplink communication user in the first iteration the second optimized value of the th downlink communication user in the first iteration and the second optimized value of the th uplink communication user in the first iteration and assign them to , , , , substitute them into the second objective function and simplify to obtain the third objective function ; where represents the introduced auxiliary variable five, and , represents the introduced auxiliary variable six, , represents the first remaining constant term; Step 5037, use a computer to make the derivative of the third objective function with respect to equal to 0 to obtain ; represents the received beamforming vector corresponding to the th uplink communication user in the first iteration; Step 5038, substitute the received beamforming vector corresponding to the th uplink communication user in the first iteration, the first optimized value of the th uplink communication user in the first iteration, the first optimized value of the th downlink communication user in the first iteration, the second optimized value of the th downlink communication user in the first iteration, and the second optimized value of the th uplink communication user in the first iteration into the second objective function and simplify to obtain the fourth objective function ; where represents the introduced auxiliary variable seven, and , represents the introduced auxiliary variable eight, and , represents the second remaining 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: ; where represents the introduced auxiliary variable nine, and , represents the introduced auxiliary variable ten, and ; Step 503A: Use a computer to solve the convex problem model using the CVX toolbox to obtain the transmission power of the -th uplink communication user in the first iteration.

[0026] In this embodiment, the specific process of step 505 is as follows: Step 5051: Based on the sensing transmit beamforming matrix in the first iteration, the communication transmit beamforming matrix corresponding to the -th downlink communication user in the first iteration, the receive beamforming vector corresponding to the -th uplink communication user in the first iteration, and the transmission power of the -th uplink communication user in the first iteration, take 1, and perform the next iteration according to steps 501 to 503A, repeat multiple times to obtain the sensing transmit beamforming matrix in the -th iteration, the communication transmit beamforming matrix corresponding to the -th iteration of the -th downlink communication user, the receive beamforming vector of the uplink communication user corresponding to the -th iteration, and the transmission power of the -th iteration of the -th uplink communication user; Step 5052: Based on the sensing transmit beamforming matrix in the -th iteration, the communication transmit beamforming matrix corresponding to the -th iteration of the -th downlink communication user, the receive beamforming vector of the uplink communication user corresponding to the -th iteration, and the transmission power of the -th iteration of the -th uplink communication user, obtain the sum of the achievable rates of all communication users in the -th iteration.

[0027] In this embodiment, during actual use, a 300m×300m area contains 1 main base station for integrated full-duplex communication and sensing, 3 secondary base stations, 1 target, 2 downlink communication users, and 2 uplink communication users. Then ; The main base station is equipped with 6 transmitting antennas and 6 receiving antennas. Then , and each secondary base station is equipped with 6 receiving antennas. Then . The transmitting antennas and receiving antennas are uniform linear arrays.

[0028] In this embodiment, during actual use, the target is set at the center of the area, and the communication users are randomly distributed with respect to the secondary base stations.

[0029] In this embodiment, during actual use, Take zero to represent the initialized sensing transmit beamforming covariance matrix and the first intermediate quantity corresponding to the initialized th downlink communication user ; Take 1 to represent the first intermediate quantity corresponding to the th downlink communication user in the first iteration and the sensing transmit beamforming covariance matrix in the first iteration , and it is obtained from the sensing transmit beamforming matrix in the first iteration, the communication transmit beamforming matrix corresponding to the th downlink communication user in the first iteration .

[0030] In this embodiment, the noise variance at the th downlink communication user takes a value of -80 dB, and the noise variances at the

[0031] th downlink communication users are the same. The noise variance at the base station for the th uplink communication user takes a value of -80 dB, and

[0032] the noise variances at the base station for the sensing noise in this embodiment takes a value of -80 dB.

[0033] In this embodiment, the dimension of the receive beamforming vector corresponding to the th uplink communication user is , and the dimension of the uplink communication channel of the th uplink communication user is , the combined sensing channel has a dimension of .

[0034] In this embodiment, the power of the residual self-interference of the main base station is -110 dB. has a dimension of , the combined self-interference channel has a dimension of , the joint active and passive sensing beam reception matrix has a dimension of , the combined beamforming matrix has a dimension of .

[0035] In this embodiment, the predefined joint active and passive sensing signal-to-interference-plus-noise ratio threshold takes a value of 10 dB, and the total power budget of the main base station is 30 dBm. The maximum power budget of the th uplink communication user

[0036] is 16 dBm. In this embodiment, the initial sensing transmit beamforming matrix , the communication transmit beamforming matrix corresponding to the th downlink communication user are both Gaussian matrices, and the receive beamforming vector corresponding to the th uplink communication user is a Gaussian vector, and the transmit power

[0037] of the th uplink communication user is a random value from 0 to 16 dBm. ; the update coefficient takes a value of .

[0038] In this embodiment, the judgment termination value takes a value of .

[0039] In summary, the method steps of the present invention are simple and reasonably designed. While ensuring sensing requirements and power, by optimizing the communication and sensing transmit beamforming matrices, the receive beamforming vectors corresponding to uplink communication users, and power allocation budgets, the sum rate of downlink and uplink transmissions is maximized, thereby facilitating the auxiliary optimization of the communication process.

[0040] The above are only the preferred embodiments of the present invention, and do not impose any limitations on the present invention. Any simple modifications, changes, and equivalent structural changes made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for multi - base - station cooperative ISAC that combines active and passive sensing, characterized in that, The method includes the following steps: Step 1, construct a multi-base-station cooperative ISAC: The multi-base-station cooperative ISAC includes a primary base station, secondary base stations, a target, and communication users. The number of secondary base stations is , and the primary base station and the secondary base stations simultaneously detect the target and are responsible for the uplink and downlink communications of the communication users. The communication users include multiple downlink communication users and multiple uplink communication users. Among them, the primary base station is equipped with transmitting antennas and receiving antennas. Each secondary base station is equipped with receiving antennas. The downlink communication users and the uplink communication users are both equipped with single antennas. The number of downlink communication users is , and the number of uplink communication users is , , , , , , and are all integers. The primary base station actively senses the echo signal of the target, and the secondary base stations passively sense the reflected signal of the target. Step 2: Obtain the achievable rates of downlink communication users and uplink communication users and the combined active and passive sensing signal-to-interference-plus-noise ratio; among them, the achievable rate of the th downlink communication user is , and the achievable rate of the th uplink communication user is , and the combined active and passive sensing signal-to-interference-plus-noise ratio is , , ; Step 3, construct a joint beamforming and transmission power optimization model: Using a computer, with the goal of maximizing the sum of the achievable rates of all communication users and subject to the constraints of ensuring the sensing service requirements and the power budgets of the primary base station and the uplink communication users, construct a joint beamforming and transmission power optimization model as follows: ; Among them, represents the combined active and passive sensing beam reception matrix, represents the th communication transmit beamforming matrix corresponding to the downlink communication user, represents the sensing transmit beamforming covariance matrix, and , represents the sensing transmit beamforming matrix, represents the th receive beamforming vector corresponding to the uplink communication user, represents the th transmit power of the uplink communication user, represents the predefined combined active and passive sensing signal-to-interference-plus-noise ratio threshold, represents the trace of the matrix, represents the total power budget of the macro base station, represents the th maximum power budget of the uplink communication user; represents the universal quantifier; represents the constraint condition; represents the maximum value; Step 4, perform three conversions on the joint beamforming and transmission power optimization model to obtain the jointly beamformed and transmission power optimized model after three conversions; Step 5, use the successive convex approximation, penalty function algorithm, and fractional programming method to iteratively obtain the optimal value for the jointly beamformed and transmission power optimized model after three conversions.

2. The method of a multi-base-station cooperative ISAC that combines active and passive sensing according to claim 1, wherein: Step 2, the specific process is as follows: Step 201, according to , obtain the signal-to-noise ratio of the th downlink communication user; where represents the communication channel from the master base station to the th downlink communication user, represents conjugate transpose, represents the communication transmit beamforming matrix corresponding to the th downlink communication user, takes a value of , and , represents the channel from the th uplink communication user to the th downlink communication user, represents the noise variance at the th downlink communication user; represents the square of the modulus; DL represents the downlink communication link; Step 202: According to , obtain the signal-to-noise ratio of the th uplink communication user ; where UL represents the uplink communication link, represents the receive beamforming vector corresponding to the th uplink communication user, , represents the receive beamforming vector from the th uplink communication user to the main base station, represents the receive beamforming vector from the th uplink communication user to the th secondary base station, is a positive integer, and , represents the transpose, represents the uplink communication channel of the th uplink communication user, , represents the communication channel from the th uplink communication user to the main base station, represents the communication channel from the th uplink communication user to the th secondary base station, represents the transmit power of the th uplink communication user, represents the uplink communication channel of the th uplink communication user, and , represents the composite interference channel, and , represents the combined sensing channel, , represents the active sensing channel, represents the th passive sensing channel, represents the combined self-interference channel, , represents the residual self-interference channel, represents the element in the th row and th column of , represents the power of the residual self-interference of the main base station, represents the natural constant, represents the imaginary unit, represents the pi, represents the th transmit antenna and the The distance between the receiving antennas, , , denotes the wavelength; denotes the th zero matrix, and is of dimension ; denotes the square of the F norm, denotes the square of the 2 norm, denotes the th noise variance of the uplink communication user at the base station, denotes the combined beamforming matrix; Step 203. According to , obtain the achievable rate of the -th downlink communication user; According to , obtain the achievable rate of the -th uplink communication user ; Step 204. According to , the combined primary and passive sensing signal-to-interference-plus-noise ratio is obtained; where represents the combined primary and passive sensing beam reception matrix, and , represents the active sensing reception beamforming matrix of the primary base station, represents the passive sensing reception beamforming matrix of the -th secondary base station, , represents the corresponding beamforming matrix for communication, represents the noise variance of sensing; represents the identity matrix.

3. A method for multi-base station cooperative ISAC that combines active and passive sensing according to claim 2, characterized in that: Step 4, the specific process is as follows: Step 401: Use a computer to set the intermediate variable corresponding to the th downlink communication user as , and ; where represents the first intermediate quantity corresponding to the th downlink communication user, and , , represents the rank of the matrix, and each element in it is greater than or equal to zero; represents the second intermediate quantity corresponding to the th downlink communication user, and , represents the first intermediate quantity corresponding to the th downlink communication user, and , , each element in it is greater than or equal to zero; Set the intermediate variable corresponding to the th uplink communication user to be , and , represents the first intermediate quantity corresponding to the th uplink communication user, and ; Set the third intermediate variable , ; where represents the perceived noise; Step 402: Substitute the combined primary and passive sensing signal-to-interference-plus-noise ratio and the intermediate variable in Step 401 into the combined beamforming and transmission power optimization model in Step 3 for a first transformation to obtain the combined beamforming and transmission power optimization model after the first transformation as follows: ; wherein, each element in is greater than or equal to zero, satisfying the Hermitian matrix; Step 403. Use a computer to Simplify to obtain , and let the fourth intermediate variable be , and perform a first-order Taylor expansion on the fourth intermediate variable at the first intermediate quantity corresponding to the th downlink communication user in the th iteration and the th iteration of the sensing transmit beamforming covariance matrix to obtain the first first-order Taylor expansion formula , and , then ; Pair Simplify to obtain , let the fifth intermediate variable , and perform a first-order Taylor expansion on the fifth intermediate variable at the first intermediate quantity corresponding to the th downlink communication user in the th iteration and the th iteration of the sensing transmit beamforming covariance matrix to obtain the second first-order Taylor expansion formula , and , then ; where represents the sixth intermediate variable, and ; The seventh intermediate variable, and ; represents the eighth intermediate variable, and ; represents the ninth intermediate variable, and ; where is a natural number; Step 404, use a computer to set the intermediate variable corresponding to the th downlink communication user and convert it into the lower limit intermediate variable corresponding to the th downlink communication user , and ; Set the intermediate variable corresponding to the th uplink communication user to the lower limit intermediate variable corresponding to the th uplink communication user , and ; Step 405, use a computer to perform a second conversion on the jointly beamformed and transmission power optimized model after the first conversion to obtain the jointly beamformed and transmission power optimized model after the second conversion, as follows: ； Step 406: Use a computer to equivalently transform the non-convex rank-one constraint into , and transform the optimization objective into ; where represents the nuclear norm, represents the 2-norm, is the penalty factor, and ; Step 407: Use a computer to perform a first-order Taylor expansion of at the -th intermediate quantity corresponding to the -th downlink communication user in the -th iteration by using the step-by-step convex approximation method, to obtain the third first-order Taylor expansion formula , and ; represents the eigenvector corresponding to the maximum eigenvalue of ; Step 408, use a computer to perform a third conversion on the jointly beamformed and transmission power optimized model after the second conversion to obtain the jointly beamformed and transmission power optimized model after the third conversion, as follows: 。 4. The method of a multi-base station cooperative ISAC that combines 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 transmit beamforming matrix corresponding to the th downlink communication user, the receiving beamforming vector corresponding to the uplink communication user, and the transmit power of the th uplink communication user. After obtaining the first optimal value of the joint active and passive sensing beam receiving matrix, use the successive convex approximation and penalty function algorithms to iterate the jointly beamformed and transmission power optimization model after three conversions to obtain the sensing transmit beamforming matrix of the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration; Step 502: Substitute the perceptual transmit beamforming matrix of the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration into the jointly beamformed and transmit power optimized model after one transformation to obtain an updated jointly beamformed and transmit power optimized model; Step 503: Use the fractional programming method to process the updated joint beamforming and transmission power optimization model to obtain the receive beamforming vector corresponding to the th uplink communication user in the first iteration and the transmission power of the th uplink communication user in the first iteration; Step 504: Substitute the perceptual transmit beamforming matrix of the first iteration , the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration , the receive beamforming vector corresponding to the th uplink communication user of the first iteration and the transmit power of the th uplink communication user of the first iteration into the objective function in Step 3 to obtain the sum of achievable rates of all communication users in the first iteration; Step 505. According to the methods of Steps 501 to 504, based on , , , perform the next iteration, and repeat it multiple times to obtain the sum of the achievable rates of all communication users in the -th iteration and the sum of the achievable rates of all communication users in the -th iteration ; where is a positive integer. Step 506, if , then the -th iteration's perceived transmit beamforming matrix , the -th iteration's communication transmit beamforming matrix corresponding to the -th downlink communication user , the -th iteration's receive beamforming vector corresponding to the -th uplink communication user and the -th iteration's transmit power of the -th uplink communication user are used as the optimized values; otherwise, perform the next iteration according to the methods of steps 501 to 504 until the optimized values are obtained; where is a positive integer, represents the judgment termination value.

5. A method for multi-base station cooperative ISAC that combines 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 as , initialize the communication transmit beamforming matrix corresponding to the -th downlink communication user as , initialize the receive beamforming vector corresponding to the -th uplink communication user as and initialize the transmit power of the -th uplink communication user as ; and obtain the initialized sensing transmit beamforming covariance matrix and the initialized first intermediate quantity corresponding to the -th downlink communication user; where, in step 403 takes zero, and performs first-order Taylor expansion at the initialization and . Step 5012, use a computer to set the first auxiliary quantity , and , the second auxiliary quantity , and , then the matrix The eigenvector corresponding to the largest eigenvalue of is the first optimal value of the combined active and passive sensing beam reception matrix ; Step 5013, at the first optimal value of the combined active and passive sensing beam reception matrix , initialize the reception beamforming vector corresponding to the th uplink communication user as and initialize the transmission power of the th uplink communication user as . Set the initial penalty factor , and use the sequential convex approximation and penalty function algorithm to perform the first iteration on the jointly beamformed and transmission power optimization model after three transformations, obtaining the sensing transmit beamforming matrix of the first intermediate iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user of the first intermediate iteration; Step 5014: Substitute the communication transmit beamforming matrix corresponding to the th downlink communication user in the first intermediate iteration, and determine whether holds. If it does not hold, let , and execute Step 5015; if it holds, then use the sensing transmit beamforming matrix in the first intermediate iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user in the first intermediate iteration as the sensing transmit beamforming matrix in the first iteration and the communication transmit beamforming matrix corresponding to the th downlink communication user in the first iteration ; where , represents the update value in the first intermediate iteration, and represents the update coefficient; represents the maximum termination value; Step 5015: Input the first intermediate iteration update value and perform the next iteration according to the methods of Step 5013 and Step 5014 until the sensing transmit beamforming matrix corresponding to the g-th intermediate iteration when it holds and the communication transmit beamforming matrix corresponding to the -th downlink communication user in the g-th intermediate iteration are respectively used as the sensing transmit beamforming matrix of the first iteration and the communication transmit beamforming matrix corresponding to the -th downlink communication user in the first iteration ; where g is a positive integer greater than 1.

6. A method for a multi-base station cooperative ISAC that combines active and passive sensing according to claim 5, characterized in that: Step 502, the specific process is as follows: Assign the perceptual transmit beamforming matrix of the first iteration to , and assign the communication transmit beamforming matrix corresponding to the th downlink communication user in the first iteration to . Substitute it into the joint beamforming and transmission power optimization model after one transformation, and combine with Constraint 3 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 equivalently transform the objective function using Lagrangian dual transformation into the first objective function ; where represents the introduced auxiliary variable one, and represents the introduced auxiliary variable two; Step 5032: Use a computer to convert Objective Function 1 into Objective Function 2 by means of the quadratic transformation of fractional programming ; where represents the introduced auxiliary variable three represents the introduced auxiliary variable four represents taking the real part represents the conjugate represents the square of the modulus Step 5033: Use a computer to set the first introduced variable , , the second introduced variable , ; Step 5034, initialize the receive beamforming vector corresponding to the th uplink communication user as and initialize the transmit power of the th uplink communication user as Under the condition that, use a computer to make the derivative of the first objective function with respect to equal to 0, then the first optimization value of the th downlink communication user in the first iteration is obtained as and the first optimization value of the th uplink communication user in the first iteration is ; where, , ; Step 5035. Use a computer to set the derivative of the second objective function with respect to to 0, and then obtain the second optimization value of the th downlink communication user in the first iteration and the second optimization value of the th uplink communication user in the first iteration ; where , ; Step 5036, initialize the transmit power of the th uplink communication user and the first optimization value of the th downlink communication user in the first iteration , the first optimization value of the th uplink communication user in the first iteration The second optimization value of the th downlink communication user in the first iteration and the second optimization value of the th uplink communication user in the first iteration are assigned to , , , , and substituted into the second objective function and simplified to obtain the third objective function ; where represents the introduced auxiliary variable five, and , represents the introduced auxiliary variable six, , represents the first remaining constant term; Step 5037. Use a computer to make the derivative of the objective function three with respect to equal to 0, obtaining ; represents the received beamforming vector corresponding to the th uplink communication user in the first iteration; Step 5038: Substitute the receive beamforming vector corresponding to the th uplink communication user in the first iteration, the first optimization value of the th uplink communication user in the first iteration, the first optimization value of the th downlink communication user in the first iteration, the second optimization value of the th downlink communication user in the first iteration, and the second optimization value of the th uplink communication user in the first iteration into the second objective function and simplify to obtain the fourth objective function ; where represents the introduced auxiliary variable seven, and , represents the introduced auxiliary variable eight, and , , represents the remaining constant term in the second time. Step 5039, use a computer to convert the updated jointly beamformed and transmission power optimized model into a convex problem model, as follows: ; where, represents the introduced auxiliary variable nine, and , represents the introduced auxiliary variable ten, and ; Step 503A: Use a computer to solve the convex problem model using the CVX toolbox to obtain the transmit power of the th uplink communication user in the first iteration .

7. A method for multi-base station cooperative ISAC that combines active and passive sensing according to claim 6, characterized in that: Step 505, the specific process is: Step 5051: Based on the sensing transmit beamforming matrix of the first iteration , the communication transmit beamforming matrix corresponding to the th downlink communication user of the first iteration , the receiving beamforming vector corresponding to the th uplink communication user of the first iteration and the transmit power of the th uplink communication user of the first iteration , take 1, perform the next iteration according to Steps 501 to 503A, repeat multiple times, and obtain the sensing transmit beamforming matrix of the th iteration, the communication transmit beamforming matrix corresponding to the th iteration of the th downlink communication user, the receiving beamforming vector corresponding to the uplink communication user of the th iteration, and the transmit power of the th iteration of the th uplink communication user; Step 5052, based on the th iteration's perceived transmit beamforming matrix, the th iteration's th downlink communication user's corresponding communication transmit beamforming matrix, the th iteration's uplink communication user's corresponding receive beamforming vector, and the th iteration's th uplink communication user's transmit power, obtain the th iteration's sum of achievable rates of all communication users .

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