Cooperative interference processing method for enabling and inductance integrated network of movable antenna

Abstract

The invention provides a cooperative interference processing method for a movable antenna enabling and inductance integrated network. The method comprises the following steps: constructing an optimization problem under the movable antenna enabling and inductance integrated network; wherein the optimization problem is that the sum of the data rates of all the cells is maximized by alternately iteratively optimizing the transmitting beam forming of all the base stations and the position of the movable antenna, and the rate profile technology is utilized to depict the Pareto boundary between the data rates of the cells; decomposing two optimization sub-problems based on the optimization problem, and carrying out alternate iteration solution on the two optimization sub-problems until a total objective function of the optimization problem is converged; the first optimization sub-problem is used for optimizing transmitting beam forming of the base station under the condition that the positions of all the movable antennas are determined, and the second optimization sub-problem is used for optimizing the positions of the movable antennas under the condition that the transmitting beam forming of the base station is determined. According to the invention, the sum of data rates of all cells is maximized through transmitting beam forming and antenna position regulation and control, and the communication and sensing performance is improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a cooperative interference processing method for a mobile antenna-enabled integrated sensing network. Background Technology

[0002] While integrated communication and sensing networks can improve communication performance by utilizing inter-cell interference and enhance positioning accuracy by capturing rich environmental information through multi-angle observation of targets, traditional integrated communication and sensing networks deploy fixed-position antennas, failing to explore the locations of antenna spacing areas and thus not fully utilizing the degrees of freedom in continuous spatial regions. Furthermore, signal interference between different cells and multi-user collaboration further hinder the improvement of integrated communication and sensing network performance.

[0003] Movable antennas are a new type of antenna technology that can change the channel response. They can achieve precise beamforming by flexibly adjusting the antenna position. Therefore, introducing movable antennas into the communication and sensing integrated network can improve spectrum efficiency. The empowerment of the communication and sensing integrated network by movable antennas involves cooperative interference management and the trade-off between multiple cells. Since all base stations will connect the transmitted signals, channel state information and received signals to the central controller through the fronthaul link, deploying cooperative interference management strategies in the central controller can realize the performance improvement of the communication and sensing integrated network empowered by movable antennas and the performance trade-off between multiple cells.

[0004] Therefore, in this context, how to achieve performance gains in antenna-enabled communication and sensing integrated networks by configuring optimal transmit beamforming and antenna positions, and how to characterize the data rate trade-off between multiple cells, are problems that need to be solved in this field. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a cooperative interference processing method for a mobile antenna-enabled integrated communication and sensing network, which addresses the above-mentioned deficiencies of the prior art. The method aims to solve the problems of how to achieve performance gains in antenna-enabled integrated communication and sensing networks by configuring optimal transmit beamforming and antenna positions, and how to characterize the data rate trade-off relationship between multiple cells.

[0006] The technical solution adopted by this invention to solve the technical problem is as follows: In a first aspect, a cooperative interference processing method for a mobile antenna-enabled sensing network is provided, wherein the mobile antenna-enabled sensing network, composed of multiple cells, includes multiple base stations, and each base station is equipped with a transmitting uniform linear array carrying multiple mobile antennas and a receiving uniform linear array carrying multiple fixed-position antennas. The method includes: The optimization problem under the mobile antenna-enabled integrated sensing network is constructed; wherein, the optimization problem is to maximize the sum of data rates of all cells by iteratively optimizing the transmit beamforming of all base stations and the position of mobile antennas, and to use rate profile technology to characterize the Pareto boundary between cell data rates; The optimization problem is decomposed into two sub-problems, and the two sub-problems are solved iteratively and alternately until the overall objective function of the optimization problem converges, thus obtaining the corresponding optimization result. The two optimization sub-problems include a first optimization sub-problem and a second optimization sub-problem. The first optimization sub-problem is to optimize the transmit beamforming of the base station given all the positions of movable antennas. The second optimization sub-problem is to optimize the positions of movable antennas given the transmit beamforming of the base station.

[0007] This invention provides a cooperative interference processing method for a mobile antenna-enabled sensing network. The mobile antenna-enabled sensing network comprises multiple cells and includes multiple base stations. Each base station is equipped with a uniform linear array for transmitting and receiving, carrying multiple mobile antennas, and a uniform linear array for receiving, carrying multiple fixed-position antennas. The method includes: constructing an optimization problem for the mobile antenna-enabled sensing network; wherein the optimization problem is to maximize the sum of data rates of all cells by iteratively optimizing the transmit beamforming and mobile antenna positions of all base stations, and using rate profiling technology to characterize the Pareto boundary between cell data rates; decomposing the optimization problem into two sub-problems, and iteratively solving the two sub-problems until the overall objective function of the optimization problem converges, obtaining the corresponding optimization result; wherein the two sub-problems include a first sub-problem and a second sub-problem, and the first sub-problem is to optimize the transmit beamforming of the base station given all mobile antenna positions, and the second sub-problem is to optimize the mobile antenna positions given the transmit beamforming of the base station. Therefore, this invention introduces a movable antenna into a communication and sensing integrated network. By using transmit beamforming and movable antenna position adjustment, the sum of data rates of all cells is maximized, which can improve communication and sensing performance, improve spectrum efficiency, and use rate profile technology to characterize the Pareto boundary between cell data rates, thereby fully characterizing the data rate trade-off relationship between multiple cells. Attached Figure Description

[0008] Figure 1 This is a flowchart of a preferred embodiment of the cooperative interference processing method for a mobile antenna-enabled integrated sensing network in this invention; Figure 2 This is a schematic diagram of a specific mobile antenna-enabled integrated communication and sensing network disclosed in this invention; Figure 3 This is a schematic diagram of a specific movable antenna linear array disclosed in this invention; Figure 4 This is a schematic diagram of a specific movable area division disclosed in this invention; Figure 5 This is a schematic diagram of a specific mobile antenna-enabled integrated communication and sensing network scenario disclosed in this invention; Figure 6 This is a schematic diagram of a specific convergence curve disclosed in this invention; Figure 7 This is a schematic diagram of a specific cell rate trade-off curve disclosed in this invention; Figure 8 This is a schematic diagram of another specific cell rate trade-off curve disclosed in this invention; Figure 9 This is a schematic diagram of the curve showing the change of the total rate of all cells as a function of the base station transmit power, as disclosed in this invention. Figure 10 This is a schematic diagram of the curve showing the variation of the total rate of all cells with the normalized mobile range, as disclosed in this invention. Figure 11 This is a schematic diagram of the curve showing the change of the total rate of all cells as a function of the number of movable antennas transmitted by the base station, as disclosed in this invention. Figure 12 This is a schematic diagram of the curve showing the change of the total rate of all cells as a function of the number of base station receiving antennas, as disclosed in this invention. Figure 13 This is a schematic diagram of the curve showing the change of the total rate of all cells as a function of the sensing threshold, as disclosed in this invention. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0010] Please see Figure 1 , Figure 1 This is a flowchart of the cooperative interference processing method for a mobile antenna-enabled integrated sensing network in this invention. Figure 1As shown in the embodiment of the present invention, the cooperative interference processing method for a mobile antenna-enabled sensing integrated network includes multiple base stations in the mobile antenna-enabled sensing integrated network composed of multiple cells. Each base station is equipped with a transmitting uniform linear array carrying multiple mobile antennas and a receiving uniform linear array carrying multiple fixed-position antennas. The method includes: Step S11: Construct the optimization problem under the mobile antenna-enabled integrated sensing network; wherein, the optimization problem is to maximize the sum of data rates of all cells by iteratively optimizing the transmit beamforming and mobile antenna positions of all base stations, and to use rate profile technology to characterize the Pareto boundary between cell data rates.

[0011] In this embodiment, based on the need to maximize the sum of data rates of all cells by configuring optimal transmit beamforming and antenna positions, and to characterize the trade-off relationship of data rates among multiple cells, an optimization problem is constructed for a mobile antenna-enabled sensing integrated network. This problem involves iteratively optimizing the transmit beamforming and mobile antenna positions of all base stations to maximize the sum of data rates of all cells. It should be noted that the cooperative communication and sensing integrated network (sensing integrated network) can achieve cooperative communication and sensing functions by connecting distributed base stations through fronthaul links, and utilizes inter-cell interference and multi-angle observation to obtain performance gains.

[0012] For example, see Figure 2 As shown, by A mobile antenna-enabled integrated communication and sensing network, composed of several cells, includes... There are 1 base station, one base station is deployed in each cell, and each base station is equipped with a... A uniform linear array of movable antennas and a transmission array with A uniform linear array of antennas at fixed locations. In this embodiment, based on a cloud wireless access network architecture, all base stations are connected to the central controller via a fronthaul link, and each base station transmits data to... Each single-antenna associated user sends a dual-function signal for communication, while all base stations receive the echo signal reflected by the single-antenna target and transmit it to the central controller for collaborative sensing.

[0013] Among them, base stations The first The expression for the position of each movable antenna is: (1) Therefore, base station The antenna position vector of the movable antenna can be expressed as: ; in, Indicates base station The first One movable antenna, Indicates base station The first The location of a movable antenna Indicates the movable area. , Indicates the total number of base stations. This represents the set of indices for movable antennas at a base station. And see the linear array of movable antennas. Figure 3 As shown.

[0014] Furthermore, the distance between two adjacent movable antennas is not less than the minimum tolerable distance, which can avoid the coupling effect of the movable antenna array, and its expression is: (2) in, Indicates base station The first The location of a movable antenna This represents the minimum tolerable distance.

[0015] It should be noted that, considering the movable area of ​​a mobile antenna is much smaller than the signal propagation distance, the channel can be modeled as a far-field wireless channel. It is assumed that all mobile antennas at the same base station have the same signal propagation direction and amplitude, but different phases. Therefore, the channel can be modeled as a function of the mobile antenna's position, and higher channel gain can be obtained by adjusting the position of the mobile antenna. It is assumed that the communication channel is modeled as a narrowband quasi-static channel, i.e., the transmitter and receiver are static or move slowly. Therefore, the time overhead of moving the mobile antenna is negligible compared to the longer channel coherence time. The communication channel adopts the classic multipath channel model.

[0016] In this embodiment, the dual-function signal transmitted by the base station is used for communication and sensing. transmission signal The expression can be: ; The transmit power of each base station shall not exceed the maximum transmit power budget, and the expression is as follows: ; (10) That is, the transmit power of each base station satisfies the above expression (10).

[0017] in, Indicates base station The One associated user, i.e., base station The The associated user is represented as the _th One user, Indicates from base station To users The transmitted beamforming, and , Indicates launch data. Indicates the number of movable antennas. This represents the maximum transmit power budget for each base station.

[0018] when It is a complex cyclic Gaussian variable with a mean of 0 and a variance of 1. Therefore, the user The expression for the received signal can be: ; in, Indicates user The additive white Gaussian noise has an average noise power of .

[0019] From the user The expression for the received signal shows that each user is affected by intra-area interference, inter-area interference, and noise.

[0020] In this embodiment, active sensing and passive sensing are considered, and the base station The expression for the received cooperative sensing signal can be: ; in, It is a base station Additive white Gaussian noise, obeys .

[0021] The perceived signal-to-noise ratio (SNR) of each base station is not less than a preset SNR threshold, which can guarantee the sensing performance, and its expression is: (17) in, Indicates base station The perceived signal-to-noise ratio at that location. This indicates the transmit beamforming of the base station. Indicates the location of the movable antenna. This indicates the preset signal-to-noise ratio threshold.

[0022] In this embodiment, the base station The expression for the perceived signal-to-noise ratio at a given point is: ; .

[0023] In this context, for the sensing channel, it is assumed that only a line-of-sight path exists from each base station to the target. Because the target's size is sufficiently small compared to the round-trip distance, the target can be considered as a point. Therefore, the base station... The launch steering vector at the location and receiving guide vector They are respectively: ; ; ; ; in, Indicates from base station Reflected by the target, reaching the base station The channel, and , Indicates base station Send to user Transmit beamforming, Indicates perceived noise. The complex channel coefficients represent the target radar cross-sectional area and round-trip path loss. Indicates base station Launch guidance at the location, Indicates base station The receiving guidance at the location, Indicates antenna spacing. It is a base station The starting angle at that point, Indicates base station The angle of arrival at the location, Indicates the carrier wavelength.

[0024] In this embodiment, the cell data rate region of the cooperative communication and sensing integrated network is defined as a rate tuple. This rate tuple contains all achievable cell data rate combinations under constraints (1), (2), (7), and (10), and its expression can be: ; The boundary formed by all Pareto optima in the aforementioned region is called the Pareto boundary. At the Pareto boundary, increasing the data rate of a particular cell will inevitably reduce the data rate of other cells.

[0025] In this embodiment, rate profiling technology is used to fully characterize the Pareto boundary between cell rates. It should be noted that traditional weighted sum rate maximization methods combine multiple rates into a single objective function using weighting factors, but single-objective optimization struggles to find all points on the Pareto boundary. Rate profiling, essentially a multi-objective optimization technique, considers the trade-offs between multiple objectives simultaneously, thus characterizing the complete Pareto boundary. Specifically, the fairness factor is defined as: ; Therefore, all rate tuples on the Pareto boundary can be solved by addressing a specific fairness factor. The optimization problem is obtained as follows, where the expression for the optimization problem is: (19) ; ; ; ; in, Describe the overall objective function. Indicates constraint by, Indicates the first The ratio of the data rate of one cell to the target total data rate of all cells. Indicates base station The associated user index set, Indicates the total number of users. Indicates the first Data rate of each cell Indicates user Data rate.

[0026] user The expression for the data rate is: ; ; ; in, Indicates user Signal-to-interference-to-noise ratio, Indicates base station and users The channel between, This indicates the conjugate transpose. Indicates user Interference, This represents the average noise power.

[0027] base station and users The expression for the channel between them is: (4) ; .

[0028] in, Indicates from base station The number of paths to each associated user From base station To users The Complex channel coefficients of each path, Indicates base station To users The The launch guidance of each propagation path, Indicates from base station To users The The starting angle of the path.

[0029] Step S12: Based on the optimization problem, decompose it into two optimization sub-problems, and solve the two optimization sub-problems alternately and iteratively until the overall objective function of the optimization problem converges to obtain the corresponding optimization result; wherein, the two optimization sub-problems include a first optimization sub-problem and a second optimization sub-problem, and the first optimization sub-problem is to optimize the transmit beamforming of the base station given all the positions of movable antennas, and the second optimization sub-problem is to optimize the positions of movable antennas given the transmit beamforming of the base station.

[0030] In this embodiment, due to the fractional nature of constraint (19a) and the coupling variables of constraints (19a) and (17), i.e., due to the high coupling of variables and the non-convexity of the problem, expression (19) is difficult to solve. Therefore, the fractional nature of expression (19) can be solved by introducing auxiliary variables, and an effective substitution optimization algorithm can be used to solve expression (19). That is, the optimization problem is decomposed into two optimization sub-problems, and the two optimization sub-problems are solved iteratively until the overall objective function of the optimization problem converges, and the corresponding optimization result is obtained. It is understood that to solve the data rate maximization problem of all cells based on the effective algorithm of substitution optimization, it is necessary to iteratively optimize the transmit beamforming and the position of the movable antenna.

[0031] In this embodiment, two optimization sub-problems are decomposed based on the optimization problem. Specifically, this may include: using preset slack variables to decouple the numerator and denominator of the constraint (19a) in the expression (19) of the optimization problem to obtain a new expression of the optimization problem. The new expression for the optimization problem is: ;(twenty one) ; ; in, and Indicates the pre-defined slack variable. and Indicates user The corresponding element.

[0032] Determine (21b) using the continuous convex approximation The convex upper bound; the expression for the convex upper bound is: ;(twenty two) Substituting the expression for the convex upper bound (22) into the constraint (21b) of the new expression (21) of the optimization problem, we obtain the simplified expression: ; (twenty three) Based on the simplified expression, two optimization subproblems are decomposed. It is understandable that by introducing auxiliary variables to decouple the numerator and denominator in constraint (19a), constraint (19a) can be transformed into constraints (21a), (21b), and (21c). The constraint in constraint (21b)... ,about and Since the problem is not jointly convex, the convex upper bound can be obtained using the continuous convex approximation method. Then, the expression (22) is substituted into the constraint (21b) to obtain the simplified expression of the optimization problem. Due to the high coupling of variables, the simplified expression (23) of the optimization problem can be solved iteratively based on the AO algorithm, that is, each variable is updated given the other variables. Therefore, problem (23) can be decomposed into two subproblems.

[0033] In this embodiment, two optimization sub-problems are decomposed based on the simplified expression. Specifically, this may include: determining the positions of all movable antennas based on the simplified expression to decompose the first optimization sub-problem, the expression of which is: ;(twenty four) That is, given Expression (23) can be rewritten as expression (24).

[0034] Furthermore, based on the simplified expression, the transmit beamforming of the base station is determined to decompose the second optimization subproblem, the expression of which is: (28) That is, given and Expression (23) can be rewritten as expression (28), that is, The optimization subproblem is expressed as expression (28).

[0035] In this embodiment, solving the first optimization subproblem may specifically include: determining the constraints (23a) in the expression of the first optimization subproblem. The lower bound function, that is, since The convexity property can be derived from... The lower bound function is: (25) ; express In the Local points in the next iteration; right At local points Applying the first-order Taylor expansion, we obtain the constraint (21d) in... The lower bound function, The lower bound function is: (26) ; based on The lower bound function and The lower bound function approximates the first optimization subproblem as the first convex problem; the expression for the first convex problem is: (27) The first convex problem is solved using a standard solver, yielding the optimal solution to the first optimization subproblem. It should be noted that the variables in expression (27)... Since they are independent of each other and the expression (27) is convex with respect to each variable, the expression (27) can be solved using a standard solver, such as the CVX optimization tool.

[0036] Understandably, due to the non-convexity of constraints (21d) and (23a), expression (24) remains non-convex. To solve this optimization problem, a continuous convex approximation technique is used to iteratively solve expression (24). That is, at any given local point in each iteration, an approximate convex constraint of constraints (21d) and (23a) is constructed, and a series of approximate convex problems are solved iteratively to obtain a suboptimal solution to expression (24).

[0037] For example, let Representing local points In the Optimal transmit beamforming in the next iteration and yes and In the The optimal solution for the nth iteration. Then an efficient iterative algorithm can be used to solve expression (24). Specifically, when the number of iterations... At that time, at a local point Find the optimal solution to problem (27) and update the transmitted beamforming to ,Right now ,in, This indicates the initial transmitted beamforming. Similarly, there is... and ,in, and It is the initial one. and .

[0038] It should be pointed out that, and All include That is to say, the constraints (23a), (21c), and (21d) of expression (28) contain the following: Then the optimization variables can be explicitly expressed. In order to determine whether a variable contains Only when the variable is Add only when necessary. .

[0039] In this embodiment, solving the second optimization sub-problem may specifically include: Base station and users The expression for the channel between them (4) is rewritten as follows: (5) ; Rewrite expression (5) as follows: (30) in, The The line only depends on , for Except for the The stacking of other rows besides the row, which is related to Irrelevant for Except for the Stacking of other rows besides the first row, for The Each element.

[0040] In constraint (23a) Expanded into the following expression: (31) In constraint (21d) Expanded into the following expression: (32) in, ; ; Understandably, due to Included in base station It is included in the intra-area interference items, but is also included in the inter-area interference items of other base stations. For base stations constraint (21c) It can be expanded as follows: ; (33) For base stations Constraint (21c) It can be expanded as follows: (34) Therefore, constraint (21c) can be rewritten as the following expression: ; (35) Rewrite constraint (23a) as follows: ; (36) The constraint (21d) is expanded into the following expression: (37) It should also be noted that, and for Since it is neither convex nor concave, constraints (35), (36), and (37) are still difficult to handle. Based on the continuous convex approximation technique, it is necessary to construct the lower bound concave function on the left side of constraints (36) and (37) and the upper bound convex function on the left side of constraint (35), respectively.

[0041] Therefore, for and exist local points Applying a second-order Taylor expansion, we obtain the following expression: (38) (39) ; (40) ; ; Based on expressions (31) and (32), it can be determined that: ; ; in, Between and between; The first derivative is: (41) The first derivative is: (42) Determine the approximate convex constraints of expression (35): (43) Determine the approximate convex constraints of expression (36): (44) Determine the approximate convex constraints of expression (37): ; (45) (46) (47) in, and It is a positive number.

[0042] Based on expressions (43), (44), and (45), the second optimization subproblem is transformed into a second convex problem, the expression of which is: ; (48) The second convex problem is solved using a standard solver to obtain the optimal solution to the second optimization subproblem.

[0043] It should be noted that in transforming the second optimization subproblem into a second convex problem, the significant time overhead caused by long-distance antenna movement in real-world scenarios was also considered. Therefore, the movable area was... Divided into Sub-regions See Figure 4 As shown, that is, each movable antenna Only allowed in sub-regions China Mobile. Since each movable antenna is independent of the others, it can be assumed without loss of generality that... So about The position optimization problem (the second sub-optimization problem) can be transformed into the above expression (48).

[0044] It is understandable that in this embodiment, the alternating optimization algorithm is used to solve expression (19). Due to the repeated use of the continuous convex approximation, the quality of the solution largely depends on the selection of the initial point. Therefore, the overall algorithm for this optimization problem includes two stages: initial point search and alternating optimization of transmit beamforming and antenna position. For example, in the initial point search stage: firstly, 500 antenna position samples that meet the constraints are generated as search points, and the index set is... For each sample Solve expression (24) (the first optimization subproblem) and obtain the corresponding solution. Then compare To obtain the maximum value and select the corresponding samples. As the initial point In the alternating optimization stage of transmit beamforming and antenna position: firstly, solve expression (24) to obtain the given... The optimal transmit beamforming matrix is ​​determined, and then the continuous convex approximation is used to solve expression (28) (the second optimization subproblem) to optimize the positions of all transmit movable antennas. The above optimization subproblems are solved alternately until... The increment is lower than This means that the overall objective function of the optimization problem converges.

[0045] As can be seen, in this embodiment of the invention, the introduction of a movable antenna into the integrated communication and sensing network, through transmit beamforming and movable antenna position adjustment, maximizes the sum of data rates of all cells, which can improve communication and sensing performance, improve spectrum efficiency, and use rate profile technology to characterize the Pareto boundary between cell data rates, thereby fully characterizing the data rate trade-off relationship between multiple cells.

[0046] For example, see Figure 5 As shown, the mobile antenna-enabled sensing integrated network, composed of multiple cells, includes multiple base stations, specifically: having The mobile antennas of base stations enable integrated communication and sensing networks, in which each base station serves... There are a total of [number] users, meaning the system has a total of [number] users. Each user, each base station is equipped with a device containing A uniform linear array of movable antennas and a array containing A uniform linear array of fixed-position antennas is used, and the minimum distance between movable antennas is set to... ,in m, noise power set to dBm, dBm, the coordinates of the three base stations are set to (500 m, 250 m, respectively). m), (1250m, 500 (m) and (500 m, 750 m) m).

[0047] Consider a geometric channel model where the number of transmit and receive paths is the same, i.e. Each base station and user The complex channel gain between them is determined by Given, among which, dB is the free path loss at a reference distance of 1m. This represents the path loss index.

[0048] Assuming that elevation angle and azimuth angle, departure angle and arrival angle are independent and identically distributed variables, in The area is evenly distributed. Furthermore, the target area is set at points (750m, 500m). A circular area centered at m and with a radius of 100m. Base station Target base station The channel coefficient of the link is determined by Provided.

[0049] in, The convergence threshold of the algorithm is set to be related to the radar cross-sectional area. , , , , as well as .

[0050] The performance of the technical solution of this application is compared with the following benchmark solutions: i) Communication Only: In this scheme, sensing performance is not considered, that is, all transmitted signals are used only for communicating with the user.

[0051] ii) Fixed antenna positions: The base station is equipped with a uniform linear array of antennas in fixed positions, containing There are 1 antenna, spaced apart by 1. .

[0052] iii) Random antenna locations: 50 are generated in each channel implementation. Random sample. For each location sample, Satisfy constraints (48a) to (48c), and in the given... The transmit beamforming is then optimized. The best-performing solution is selected from these 50 samples as the output of the proposed scheme.

[0053] iv) Random antenna position, maximum ratio transmission (MRT): In this scheme, the MRT method can be used to precode the transmit beamforming matrix, thereby maximizing the received signal power in the ideal direction. Therefore, the... The MRT precoding vectors of each user are composed of Give it. Then, 50 will be generated. Solving these samples using random samples The problem involves power control. The optimal solution is selected from these 50 samples as the output of the proposed solution.

[0054] See Figure 6 As shown, it demonstrates when The convergence of the proposed scheme and the baseline scheme is compared. As the number of iterations increases, the total achievable rate of all schemes gradually increases and eventually tends to a stable value. Since the baseline scheme only updates the transmit beamforming, while the proposed scheme optimizes the transmit beamforming and antenna position through alternating iterations, the proposed scheme can achieve a higher total data rate than the baseline scheme.

[0055] See Figure 7 As shown, the Pareto boundary of the cell rate in the two-cell case is depicted, and this boundary is determined by changing the fairness factor. Obtained. Specifically Adjust from 0.05 to 0.95 in increments of 0.05. Set accordingly This trade-off curve shows that when the rate in one cell increases, the rate in another cell will decrease. Furthermore, when... At that time, the Pareto optimal points of different schemes were determined, and the results showed that when the cell contour factor was the same, the achievable rates of each cell were equal. Moreover, the Pareto optimal point of the scheme proposed in this application fell on the Pareto boundary, that is, the scheme proposed in this application can achieve excellent interference management. The random antenna position-MRT scheme performed the worst because it did not optimize the transmit beamforming, resulting in severe inter-cell interference.

[0056] See Figure 8As shown, the Pareto boundary is extended to the three-cell scenario. The results show that the proposed scheme effectively characterizes the Pareto boundary in the three-cell scenario and arrives at similar conclusions as in the two-cell scenario.

[0057] See Figure 9 As shown, the relationship between the achievable total rate of all cells and the maximum power budget of each base station is illustrated. Since the increase in transmit power directly leads to enhanced signal strength, it compensates to some extent for the signal attenuation caused by channel fading, thereby improving the achievable rate. Therefore, with the increase of transmit power, all schemes can achieve higher total rates. Using the scheme proposed in this application, it is compared with random location schemes and fixed location schemes to demonstrate the performance improvement brought about by antenna location optimization. To evaluate the superiority of transmit beamforming optimization, it is observed that the random location scheme achieves a higher total rate than the random location scheme using MRT. This is because MRT lacks specific suppression or cancellation measures for inter-cell interference, which severely degrades the quality of the received signal, leading to a decline in system performance.

[0058] See Figure 10 As shown, the relationship between the achievable total rate of all cells and the normalized area size is illustrated. The proposed scheme significantly improves communication and sensing performance by enabling flexible array response design from a larger movable area size through joint transmit beamforming and antenna position optimization. While the fixed-position scheme also achieves optimal beamforming design, it can only achieve a limited rate due to the fixed antenna position. Apart from the fixed antenna position, all schemes achieve higher rates with increasing movable area size. This is because antenna movement can significantly configure the distribution of multipath signals to find more favorable multipath combinations for signal transmission, thereby improving received signal quality. However, when the area size further increases beyond a certain size threshold, the performance improvement is smaller. This is because when the movable area is large enough, the multipath environment becomes relatively stable, and there is no significant difference between multipath signals in larger and smaller area sizes, which limits further performance improvements.

[0059] See Figure 11 As shown, the achievable total rate of all cells under different numbers of transmit antennas at the base station was analyzed, demonstrating that increasing the number of transmit antennas improves the total rate. This is because with an increase in the number of transmit antennas, the signal is transmitted through multiple different antennas, forming a narrower and more directional beam, thus directing the beam towards the target user and increasing the desired signal power with less interference. Furthermore, with an increase in the number of transmit antennas, the performance gap between the proposed scheme and the random location scheme decreases. Additionally, when... In this case, the performance of a fixed-position antenna is lower than that of a random-position + MRT scheme. This is because in a fixed-position antenna scheme with a very small number of antennas, the beamforming direction and shape are relatively limited, and the spatial degrees of freedom are not fully utilized.

[0060] See Figure 12 As shown, when the number of antennas is less than 4, the total rate increases with the increase in the number of receiving antennas, while it remains unchanged when the number of antennas is greater than 4. The former is because more receiving antennas result in higher received power, satisfying sensing performance and thus improving the flexibility of beamforming design. The latter is because sensing performance is basically satisfied, but power is limited. Furthermore, the upper bound of the total rate is independent of the number of receiving antennas because this scheme does not consider sensing performance.

[0061] See Figure 13 As shown, the total rate of all cells varies with the sensing threshold, indicating that the total rate of all schemes decreases as the sensing threshold increases. This is because a higher sensing threshold means that the communication-sensing integrated network requires a higher signal strength to detect targets, which limits the flexibility of beamforming design and reduces communication performance.

[0062] In summary, this application utilizes movable antennas to enable integrated communication and sensing networks. By jointly designing transmit beamforming and antenna positions, it fully explores the degrees of freedom in the continuous array space, thereby achieving more precise beamforming, greatly improving spectral efficiency. Furthermore, combined with cooperative interference management strategies, it can fully characterize the rate trade-off relationship between multiple cells, while significantly improving communication and sensing performance.

[0063] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0064] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A cooperative interference processing method for a mobile antenna-enabled integrated sensing network, characterized in that, The mobile antenna-enabled sensing network, composed of multiple cells, includes multiple base stations, and each base station is equipped with a transmitting uniform linear array carrying multiple mobile antennas and a receiving uniform linear array carrying multiple fixed-position antennas. The method includes: The optimization problem under the mobile antenna-enabled integrated sensing network is constructed; wherein, the optimization problem is to maximize the sum of data rates of all cells by iteratively optimizing the transmit beamforming of all base stations and the position of mobile antennas, and to use rate profile technology to characterize the Pareto boundary between cell data rates; The optimization problem is decomposed into two sub-problems, and the two sub-problems are solved iteratively and alternately until the overall objective function of the optimization problem converges, thus obtaining the corresponding optimization result. The two optimization sub-problems include a first optimization sub-problem and a second optimization sub-problem. The first optimization sub-problem is to optimize the transmit beamforming of the base station given all the positions of movable antennas. The second optimization sub-problem is to optimize the positions of movable antennas given the transmit beamforming of the base station.

2. The cooperative interference processing method for a mobile antenna-enabled integrated sensing network according to claim 1, characterized in that, base station The first The expression for the position of each movable antenna is: ；（1） in, Indicates base station The first One movable antenna, Indicates base station The first The location of a movable antenna Indicates the movable area. Indicates the total number of base stations. This represents the set of indices representing movable antennas at a base station; Furthermore, the distance between two adjacent movable antennas is not less than the minimum tolerable distance, and the expression is: ；（2） in, Indicates base station The first The location of a movable antenna Indicates the minimum tolerable distance; The transmit power of each base station shall not exceed the maximum transmit power budget, and the expression is as follows: ；（10） in, Indicates base station The One associated user, Indicates from base station To users The transmitted beamforming, and , Indicates the number of movable antennas. This represents the maximum transmit power budget for each base station; The perceived signal-to-noise ratio (SNR) of each base station is not less than a preset SNR threshold, and the expression is: ；（17） in, Indicates base station The perceived signal-to-noise ratio at that location. This indicates the transmit beamforming of the base station. Indicates the location of the movable antenna. This indicates the preset signal-to-noise ratio threshold.

3. The cooperative interference processing method for a mobile antenna-enabled integrated sensing network according to claim 2, characterized in that, base station The expression for the perceived signal-to-noise ratio at a given location is: ； ； ； ； ； ； in, Indicates from base station Reflected by the target, reaching the base station The channel, Indicates base station Send to user Transmit beamforming, Indicates perceived noise. The complex channel coefficients represent the target radar cross-sectional area and round-trip path loss. Indicates base station Launch guidance at the location, Indicates base station The receiving guidance at the location, Indicates antenna spacing. It is a base station The starting angle at that point, Indicates base station The angle of arrival at the location, Indicates the carrier wavelength.

4. The cooperative interference processing method for a mobile antenna-enabled integrated sensing network according to claim 3, characterized in that, The expression for the optimization problem is: ；（19） ； ； ； ； in, Describe the overall objective function. Indicates constraint by, Indicates the first The ratio of the data rate of one cell to the target total data rate of all cells. Indicates base station The associated user index set, Indicates the total number of users. Indicates the first Data rate of each cell Indicates user Data rate.

5. The cooperative interference processing method for a mobile antenna-enabled integrated sensing network according to claim 4, characterized in that, user The expression for the data rate is: ； ； ； in, Indicates user Signal-to-interference-to-noise ratio, Indicates base station and users The channel between, This indicates the conjugate transpose. Indicates user Interference, This represents the average noise power.

6. The cooperative interference processing method for a mobile antenna-enabled integrated sensing network according to claim 5, characterized in that, base station and users The expression for the channel between them is: ；（4） ； ； in, Indicates from base station The number of paths to each associated user From base station To users The Complex channel coefficients of each path, Indicates base station To users The The launch guidance of each propagation path, Indicates from base station To users The The starting angle of the path.

7. The cooperative interference processing method for a mobile antenna-enabled integrated sensing network according to claim 6, characterized in that, The optimization problem is decomposed into two sub-problems, including: By using preset slack variables to decouple the numerator and denominator of constraint (19a) in the expression (19) of the optimization problem, a new expression for the optimization problem is obtained; The new expression for the optimization problem is: ；（21） ； ； in, and Indicates the pre-defined slack variable. and Indicates user The corresponding element; Determine (21b) using the continuous convex approximation The convex upper bound; the expression for the convex upper bound is: ；（22） Substituting the expression (22) of the convex upper bound into the constraint (21b) of the new expression (21) of the optimization problem, the new expression of the optimization problem is simplified to obtain the simplified expression; the simplified expression is: ； （23） Two optimization subproblems are decomposed based on the simplified expression.

8. The cooperative interference processing method for a mobile antenna-enabled integrated sensing network according to claim 7, characterized in that, The process of decomposing the simplified expression into two optimization sub-problems includes: Based on the simplified expression, the positions of all movable antennas are determined to decompose the first optimization sub-problem; the expression for the first optimization sub-problem is: ；（24） Furthermore, based on the simplified expression, the transmit beamforming of the base station is determined to decompose a second optimization sub-problem; the expression for the second optimization sub-problem is: 。 9. The cooperative interference processing method for a mobile antenna-enabled integrated sensing network according to claim 8, characterized in that, Solving the first optimization subproblem includes: In determining the constraints (23a) of the expression for the first optimization subproblem The lower bound function; the The lower bound function is: ；（25） ； express In the Local points in the next iteration; right At local points Applying the first-order Taylor expansion, we obtain the constraint (21d) in... The lower bound function; the The lower bound function is: ；（26） ； Based on the above The lower bound function and the The lower bound function approximates the first optimization subproblem as a first convex problem; the expression for the first convex problem is: ；（27） The first convex problem is solved using a standard solver to obtain the optimal solution to the first optimization subproblem.

10. The cooperative interference processing method for a mobile antenna-enabled integrated sensing network according to claim 9, characterized in that, Solving the second optimization subproblem includes: Base station and users The expression for the channel between them (4) is rewritten as follows: ；（5） ； Rewrite expression (5) as follows: ；（30） in, for Except for the The stacking of other rows besides the row, which is related to Irrelevant for Except for the Stacking of other rows besides the first row, for The One element; In constraint (23a) Expanded into the following expression: ；（31） In constraint (21d) Expanded into the following expression: ；（32） in, ; ； Rewrite constraint (21c) as follows: ；（35） Rewrite constraint (23a) as follows: ；（36） The constraint (21d) is expanded into the following expression: ；（37） right and exist local points Applying a second-order Taylor expansion, we obtain the following expression: ； （38） ；（39） ；（40） ； ； ； ； in, Between and between; The first derivative is: ；（41） The first derivative is: ；（42） Determine the approximate convex constraints of expression (35): ；（43） Determine the approximate convex constraints of expression (36): ；（44） Determine the approximate convex constraints of expression (37): ； （45） ；（46） ；（47） Based on expressions (43), (44), and (45), the second optimization subproblem is transformed into a second convex problem; the expression for the second convex problem is: ； （48） The second convex problem is solved using a standard solver to obtain the optimal solution to the second optimization subproblem.

Images