Bilateral movable antenna joint resource optimization method and system

Abstract

The application discloses a kind of double-sided movable antenna joint resource optimization method and system.Therein, the method includes: obtaining network state information, determines communication performance index and perception performance index based on network state information;Based on communication performance index and perception performance index, determine to maximize the weighted total rate of communication perception as optimization target, and construct constraint condition in combination with base station transmitting power constraint, user communication service quality constraint, perception target performance constraint and antenna position constraint;Based on optimization target and constraint condition, establish double-sided movable antenna joint resource optimization problem;Iterative optimization is carried out to double-sided movable antenna joint resource optimization problem using alternating optimization method, and the optimized optimization variable is obtained, and according to the optimized optimization variable.The application solves the technical problems that the spatial degrees of freedom is insufficient, the heterogeneous user service capability is limited and the communication perception performance is difficult to improve in the existing fixed antenna or single-sided movable antenna scheme.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, specifically relating to a dual-sided movable antenna joint resource optimization method for heterogeneous user integrated communication and sensing networks, which is applied to multi-base station integrated communication and sensing wireless networks. Background Technology

[0002] With the development of sixth-generation mobile communication technology, future wireless networks not only need to provide high-speed, low-latency, and highly reliable communication services, but also need to possess multiple sensing capabilities such as target detection, localization, environmental reconstruction, and intelligent interaction. Traditional communication systems and radar sensing systems are typically deployed using independent spectrum resources and hardware platforms, which not only increases system construction and maintenance costs but also results in low spectrum resource utilization efficiency. Integrated Sensing and Communication (ISAC) technology, by sharing spectrum resources, radio frequency front-ends, and signal processing flows, enables base stations to perform target sensing and environmental detection while completing communication transmission tasks, thereby improving spectrum utilization and hardware efficiency. This has become one of the important research directions for sixth-generation mobile communication systems.

[0003] In recent years, numerous research efforts have been conducted by scholars both domestically and internationally regarding resource optimization in integrated communication and sensing systems. These efforts primarily focus on communication beamforming, sensing beamforming, power allocation, and multi-objective resource collaborative optimization. However, most existing research is still based on fixed antenna arrays, meaning the positions of antenna elements remain unchanged after system deployment. The system can only perform beam optimization under predetermined array structures and spatial response conditions. Due to the lack of dynamic adjustment capabilities for the array geometry, the system struggles to proactively reconstruct channel characteristics and spatial degrees of freedom based on changes in user location, target movement, and complex electromagnetic environments. This limits communication link enhancement, interference suppression, and target illumination capabilities. Therefore, relying solely on traditional beamforming and power control methods is insufficient to fully unlock the performance potential of future integrated communication and sensing systems.

[0004] To overcome the limitations of fixed arrays in terms of spatial freedom, movable antennas and fluid antennas have been increasingly introduced into wireless communication systems. Unlike traditional fixed antennas, movable antennas can dynamically adjust their position within a limited area. By altering the array geometry and channel response characteristics, they provide new spatial degrees of freedom for wireless systems, enabling more flexible resource allocation and performance optimization. Related research shows that antenna position optimization can effectively improve channel gain, reduce inter-user interference, and enhance the system's spectral and energy efficiency, thus becoming a research hotspot in the field of wireless communication in recent years.

[0005] Some studies have deployed fluid antennas in multi-user downlink communication systems. By jointly optimizing the user-side antenna positions and the base station beamforming matrix, the base station transmit power is reduced while meeting the minimum signal-to-interference-plus-noise ratio (SINR) constraint for users. This demonstrates that optimizing the user-side antenna positions can effectively improve multi-user communication links and verifies the advantages of fluid antennas in improving system energy efficiency and reducing interference. However, this approach is mainly geared towards pure communication systems and does not consider sensing beam design, sensing target performance, or the coupling relationship between communication and sensing resources, making it difficult to directly apply to integrated communication and sensing scenarios.

[0006] Some studies have introduced movable antennas into integrated communication and sensing systems, proposing to achieve "flexible beamforming" by adjusting the antenna position and beamforming matrix on the base station side to improve communication rate and sensing mutual information. They have also demonstrated that movable antennas can effectively enhance the synergistic gain between communication and sensing performance. However, this approach mainly focuses on the movable antenna array on the base station side and does not further consider the participation of movable antennas on the user side in optimization. It also does not cover heterogeneous user scenarios where movable antenna users and fixed antenna users coexist. Therefore, its applicability is still somewhat limited.

[0007] For traditional integrated communication and sensing systems, some studies have proposed a joint robust beamforming framework for multi-user, multi-target scenarios. By jointly designing communication beams and radar beams, the communication spectrum efficiency can be improved while ensuring sensing performance, and the robustness of the system to channel uncertainty and target parameter errors can be enhanced. However, such methods are still based on fixed antenna arrays and cannot use the antenna position degrees of freedom to change the channel structure and array configuration. Their performance improvement mainly depends on beam domain optimization, and it is difficult to further explore the potential gains in the spatial dimension.

[0008] Furthermore, existing research has addressed the joint optimization problem of antenna position and beamforming in communication-sensing integrated systems powered by movable antennas, and verified the role of movable antennas in improving the overall performance of communication-sensing. Corresponding optimal solution methods and low-complexity algorithms have been proposed, providing a theoretical basis for the application of movable antennas in communication-sensing integrated systems. However, a complete resource allocation process for multi-base station heterogeneous user networks has not yet been established. In particular, the joint optimization of communication beams, sensing beams, user-side movable antenna positions, and base station-side movable antenna positions has not been considered simultaneously, nor has the collaborative relationship between various resources in multi-user, multi-target, and heterogeneous network environments been fully studied. Therefore, there is still room for further research and improvement.

[0009] There is currently no effective solution to the above problems. Summary of the Invention

[0010] This application provides a method and system for joint resource optimization of dual-sided movable antennas, which at least solves the technical problems of insufficient spatial degrees of freedom, limited heterogeneous user service capabilities, and difficulty in synergistically improving communication sensing performance in existing fixed antenna or single-sided movable antenna schemes.

[0011] According to one aspect of the embodiments of this application, a method for joint resource optimization of dual-sided movable antennas for heterogeneous user integrated communication and sensing networks is provided, comprising: acquiring network state information of a multi-base station heterogeneous user integrated communication and sensing network; establishing a communication channel and received signal model and a sensing target sensing signal model based on the network state information; determining communication performance indicators and sensing performance indicators based on the communication channel and received signal model and the sensing target sensing signal model; determining, based on the communication performance indicators and the sensing performance indicators, maximizing the communication-sensing weighted total rate as the optimization objective, and constructing constraint conditions by combining base station transmit power constraints, user communication service quality constraints, sensing target performance constraints, and antenna position constraints; establishing a dual-sided movable antenna joint resource optimization problem based on the optimization objective and constraint conditions, using communication beamforming variables, sensing beamforming variables, user-side movable antenna position variables, and base station-side movable antenna position variables as joint optimization variables; iteratively optimizing the dual-sided movable antenna joint resource optimization problem using an alternating optimization method to obtain optimized joint optimization variables; and configuring resources for the multi-base station heterogeneous user integrated communication and sensing network according to the optimized joint optimization variables.

[0012] According to another aspect of the embodiments of this application, a dual-sided movable antenna joint resource optimization system for heterogeneous user integrated communication and sensing networks is also provided, comprising: a model building module configured to acquire network state information of a multi-base station heterogeneous user integrated communication and sensing network, establish a communication channel and received signal model and a sensing target sensing signal model based on the network state information, and determine communication performance indicators and sensing performance indicators based on the communication channel and received signal model and the sensing target sensing signal model; and a constraint building module configured to determine, based on the communication performance indicators and the sensing performance indicators, maximize the communication-sensing weighted total rate as the optimization objective, and combine base station transmission... The system constructs constraint conditions based on power constraints, user communication service quality constraints, sensing target performance constraints, and antenna position constraints. A determination module is configured to establish a joint resource optimization problem for dual-sided movable antennas, using communication beamforming variables, sensing beamforming variables, user-side movable antenna position variables, and base station-side movable antenna position variables as joint optimization variables, based on the optimization objectives and constraints. An optimization module is configured to iteratively optimize the dual-sided movable antenna joint resource optimization problem using an alternating optimization method to obtain optimized joint optimization variables, and then configure resources for the multi-base station heterogeneous user communication and sensing integrated network based on these optimized joint optimization variables.

[0013] In the embodiments of this application, the above-described solution solves the technical problems of insufficient spatial freedom, limited heterogeneous user service capabilities, and difficulty in synergistically improving communication sensing performance in existing fixed antenna or single-sided movable antenna solutions. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0015] Figure 1 This is an optional system model diagram according to an embodiment of the present application, showing the base station, fixed antenna user, movable antenna user, sensing target, communication link, sensing link and movable antenna array structure on the base station side in the multi-base station heterogeneous user sensing integrated network of the present application;

[0016] Figure 2 This is an optional network topology diagram according to an embodiment of this application, showing the spatial distribution of base stations, fixed antenna users, mobile antenna users and sensing targets in a two-dimensional area under a simulated scenario;

[0017] Figure 3This is a system achievable and rate comparison diagram under different base station transmit power conditions according to an embodiment of this application, used to compare the system performance of fixed antenna scheme, base station-side mobile scheme, user-side mobile scheme and the proposed scheme under different transmit power.

[0018] Figure 4 This is a system performance comparison chart under different movable antenna user ratios according to an embodiment of this application, used to illustrate the impact of changes in the movable antenna user ratio on the system reachability and the average reachability of movable antenna users.

[0019] Figure 5 This is a comparison chart of the worst-case perception target rate under different optional conditions of the number of perception targets according to an embodiment of this application, used to compare the perception performance of different schemes in a multi-target perception scenario.

[0020] Figure 6 This is a system performance composition comparison diagram under different optional schemes according to the embodiments of this application, used to show the compositional differences of each scheme in terms of mobile antenna users and rates, fixed antenna users and rates, and sensing and rates. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] This application addresses heterogeneous network scenarios involving mobile antenna users, fixed antenna users, and aerial sensing targets. By jointly optimizing the communication beam, sensing beam, user-side mobile antenna positions, and base station-side mobile antenna positions, it achieves a synergistic improvement in communication transmission and target sensing performance. In a multi-base station integrated communication and sensing network, this application improves the overall weighted performance of the system's communication and sensing capabilities while satisfying constraints on base station transmit power, communication service quality, sensing performance, and antenna position.

[0024] This application provides a distributed multi-base station heterogeneous user communication and sensing integrated network system assisted by a movable antenna, such as... Figure 1 As shown, the system consists of B distributed base stations, M mobile antenna users, N fixed antenna users, and K aerial sensing targets. Each base station is equipped with Q antennas whose positions can be adjusted along a one-dimensional track, forming a mobile antenna array on the base station side. The antenna positions of the mobile antenna users can be adjusted in a two-dimensional plane, while the antenna positions of the fixed antenna users remain unchanged. The system simultaneously completes communication transmission and target sensing tasks using unified spectrum resources.

[0025] Each base station employs a distributed deployment approach to complete communication transmission and target sensing. To characterize the coupling relationship between communication beams, sensing beams, and antenna position variables, this application provides a system architecture combining centralized offline resource configuration and distributed physical transmission. Specifically, during the offline optimization phase, the network controller acquires global channel state information, user location information, and sensing target location information, and jointly optimizes the communication beams, sensing beams, user-side movable antenna positions, and base station-side movable antenna positions of each base station based on this global information. After optimization, the controller distributes the corresponding resource configuration results to each base station and movable antenna users. During the physical transmission phase, each base station independently transmits communication and sensing signals according to the distributed configuration, without real-time baseband joint processing or coherent joint transmission. Therefore, the communication signals and sensing signals transmitted by different base stations are superimposed at the receiving end; this application uses an incoherent power superposition method for modeling.

[0026] For the b-th base station, let the position of its q-th antenna be denoted as . ,in , To avoid coupling effects between antennas, the positions of adjacent antennas must satisfy a minimum spacing constraint.

[0027]

[0028] in Let represent the carrier wavelength. For the m-th movable antenna user, its antenna position is denoted as . and satisfy ,in This represents the feasible movement area for user m.

[0029] 1) Base station transmission signal model

[0030] Within a time slot, the complex baseband signal transmitted by the b-th base station is represented as follows:

[0031]

[0032] in, This represents the communication beamforming vector of base station b facing the mobile antenna user m. This represents the communication beamforming vector of base station b facing user n with a fixed antenna. This represents the sensing beamforming matrix of base station b. Represents the vector of the sensed transmitted signal. Communication information symbol. and satisfy

[0033] ,

[0034] The total transmit power of a base station is limited by its maximum power, therefore...

[0035]

[0036] 2) Communication Channel and Received Signal Model

[0037] 2.1) Reception Model for Mobile Antenna Users

[0038] Let the channel vector from base station b to mobile antenna user m be...

[0039]

[0040] in This represents the antenna position vector of base station b. Due to the user-side location... Antenna position on the base station side All are adjustable. It depends on two types of positional variables. Let its q-th component be...

[0041]

[0042] in Represents the large-scale fading coefficient. This indicates small-scale fading. This represents the propagation distance between the q-th antenna and user m.

[0043] Therefore, the received signal of the m-th movable antenna user is written as

[0044]

[0045] in This represents additive white Gaussian noise. In the above equation, the first term is the desired signal, the second term is communication interference from other mobile antenna users, the third term is communication interference towards fixed antenna users, and the fourth term is interference caused by perceived signal leakage. Since base stations do not perform real-time baseband joint processing and coherent joint transmission during the physical transmission phase, the receiver uses incoherent power superposition modeling for useful signals and interference signals from different base stations.

[0046] Therefore, the signal-to-interference-plus-noise ratio (SIR) of a mobile antenna user m is expressed as:

[0047] 2.2) Reception Model for Fixed Antenna Users

[0048] Let the channel vector from base station b to fixed antenna user n be .

[0049]

[0050] Unlike users with movable antennas, the location of users with fixed antennas is not included in the optimization, therefore It depends only on the antenna position on the base station side. The received signal of the nth fixed antenna user is represented as...

[0051]

[0052] in Accordingly, its signal-to-interference-plus-noise ratio is written as...

[0053]

[0054] 3) Target perception signal model

[0055] The system senses K aerial targets, and the sensing performance index corresponding to the k-th target is denoted as . The sensing signal transmitted by the b-th base station is

[0056]

[0057] In a distributed multi-base station deployment scenario, each base station independently transmits sensing waveforms and receives target echoes. For ease of subsequent optimization modeling, let the equivalent sensing channel matrix of the b-th base station for target k be denoted as...

[0058]

[0059] It comprehensively reflects the impact of the transmission link, target scattering, and echo reception link on the sensed signal.

[0060] The interference from the k-th target comes from two parts: one part comes from echoes from other targets or coupling between sensing links, and the other part comes from communication signal leakage. Let the total interference caused by the communication signal to the sensing link of target k be denoted as . Then the perceived signal-to-interference-plus-noise ratio of target k can be uniformly expressed as:

[0061]

[0062] Furthermore, communication signal leakage interference can be written as

[0063]

[0064] in This represents the equivalent communication leakage channel from base station b to target k. The embodiments of this application employ... The sensing performance of the k-th target is characterized by the sensing information rate surrogate induced by the sensing signal-to-interference-plus-noise ratio, which is used to uniformly characterize the target echo quality and the system sensing utility.

[0065] This application provides a joint resource optimization method for dual-sided movable antennas in a heterogeneous user sensing integrated network. In scenarios involving multiple base stations coordinating services for movable antenna users, fixed antenna users, and detecting multiple sensing targets, this method uses the spatial location of the movable antenna array on the base station side, the spatial location of the movable antennas on the user side, the multi-base station communication beamforming matrix, and the multi-base station sensing beamforming matrix as joint optimization variables. The optimization objective is to maximize the system's communication sensing weighted sum rate. This optimization is strictly constrained by multiple constraints, including the maximum transmit power of the base station, the quality of service for heterogeneous user communication, the minimum detection performance of sensing targets, the boundary of the antenna movement area, and the minimum physical spacing between antenna elements. This constructs a high-dimensional, strongly coupled, non-convex optimization... To effectively solve this complex model, this application designs an algorithm framework based on alternating optimization. The original problem is decoupled into three sub-problems: joint optimization of sensing beamforming, optimization of user-side antenna position, and optimization of base station-side antenna position. These sub-problems are solved iteratively. During the solution of the sub-problems, mathematical tools such as auxiliary variables, semidefinite relaxation, continuous convex approximation, Taylor first-order expansion, and penalty function method are comprehensively used to perform equivalent transformation and convexification of the non-convex objective function and complex constraints. Thus, while ensuring the monotonic convergence of the algorithm, a deep coordination between beam domain energy allocation and the degrees of freedom of the two-sided spatial position domain is achieved. Finally, a globally optimal balance between heterogeneous user communication rate and worst-case sensing target performance is simultaneously achieved in a wide transmit power range and multi-target scenarios.

[0066] Specifically, the method includes the following steps:

[0067] Step S202: Determine the joint resource optimization problem for the dual-sided movable antennas.

[0068] In the considered distributed multi-base station heterogeneous user communication and sensing integrated network, system performance is simultaneously affected by the communication beam, sensing beam, user-side movable antenna position, and base station-side antenna position. Unlike traditional fixed array systems, in the scenario of this application, not only is the transmit beam adjustable, but the user-side position and base station-side antenna position can also participate in optimization. Therefore, the system design problem can be described as a resource allocation problem jointly driven by beam domain and position domain variables.

[0069] This application's embodiments aim to maximize the total weighted communication and sensing rate of the system. In the communication part, the objective function simultaneously characterizes the transmission performance of both mobile antenna users and fixed antenna users; in the sensing part, weighted terms represent the contribution of the target sensing performance to the overall system utility. To characterize the resource competition relationship between communication and sensing tasks, weighting coefficients are introduced. A balance needs to be struck between the two types of performance. Therefore, the objective function of the joint optimization problem is written as:

[0070]

[0071] in, , and These represent the communication beam and sensing beam variables, respectively. This represents the set of locations of users with movable antennas. This represents the set of antenna locations on the base station side.

[0072] To ensure the system design meets practical constraints, the joint optimization problem must simultaneously satisfy the following limitations. First, the total transmit power of each base station must not exceed a given upper limit. Second, considering that movable antenna users are the key communication service targets optimized in this application embodiment, and their user-side antenna positions can be adjusted within a given area, this application embodiment sets a minimum signal-to-interference-plus-noise ratio (SINR) constraint for movable antenna users to ensure their basic communication service quality. For fixed antenna users, since their receiving antenna positions cannot be adjusted, link improvement mainly relies on base station-side beamforming and base station-side movable antenna array configuration optimization. To avoid inefficient resource occupation caused by allocating excessive power to fixed antenna users when maximizing the weighted total communication sensing rate, this application embodiment sets a minimum energy efficiency constraint for fixed antenna users. Third, to ensure the system has stable target sensing capabilities, minimum sensing performance constraints need to be set for each sensing target. In addition, considering that communication signals may cause leakage interference to the sensing link, the interference level of communication beams on target sensing also needs to be limited. Finally, the positions of user-side movable antennas and base station-side antennas also need to meet physical constraints such as feasible regions and minimum spacing.

[0073] Based on the above objectives and constraints, the original problem can be formulated as follows:

[0074]

[0075] The aforementioned problem is a non-convex optimization problem. Its non-convexity is mainly reflected in three aspects: First, the signal-to-interference-plus-noise ratio (SIR) constraints for mobile antenna users, the energy efficiency constraints for fixed antenna users, and the sensing performance constraints for the sensing target all have fractional structures; second, beam variables and position variables jointly affect the effective channel, leading to a strong coupling relationship between the objective function and the constraints; third, array configuration and user position adjustments simultaneously affect the desired signal term, interference term, and energy efficiency term, making it difficult to directly separate and solve these variables. Therefore, the problem cannot be directly solved using standard convex optimization methods and requires further design of a block-based iterative algorithm.

[0076] Step S204: Optimize variables by using a combined beamforming and antenna position optimization algorithm.

[0077] Because the original problem involves coupling relationships between the communication beam, sensing beam, the location of the movable antenna on the user side, and the location of the antenna on the base station side, and because both the objective function and constraints contain non-convex fractional performance expressions, the original problem is difficult to solve directly. Therefore, this application employs an alternating optimization framework, decomposing the original problem into three sub-problems for iterative solving: optimizing beamforming with fixed location variables; optimizing the location of the movable antenna on the user side with fixed beam and base station antenna locations; and optimizing the location of the base station antenna with fixed beam and user side locations. By alternately updating these three sub-problems, the objective function value of the original problem can be gradually improved.

[0078] Specifically, the method for optimizing variables includes the following steps:

[0079] Step S2042: Determine the overall solution framework.

[0080] Let the optimization variables in the t-th outer iteration be the beam variables. , , Variable position of movable antenna on the user side and base station side antenna position variables In each outer iteration, the embodiments of this application sequentially perform the following three steps.

[0081] 1) Fixed and Optimize communication beams and sensing beams;

[0082] 2) Fixed beam variables and Optimize the location of the movable antenna user terminal ;

[0083] 3) Fixed beam variables and Optimize the antenna position at the base station .

[0084] Since each step updates only a subset of variables, the original high-dimensional coupled problem is transformed into three more clearly structured subproblems. This alternating optimization process continues until the objective function increment between two adjacent outer iterations falls below a preset threshold.

[0085]

[0086] Step S2042, beamforming optimization subproblem.

[0087] At the fixed movable antenna user end antenna position and the location of the base station antenna Afterwards, both the communication channel and the sensing equivalent channel can be considered as known constants. At this point, the original problem can be transformed into an optimization problem concerning the communication beam and the sensing beam, i.e.

[0088]

[0089] The non-convexity of this subproblem mainly stems from the logarithmic rate objective function, the fractional signal-to-interference-plus-noise ratio (SINR) constraint for mobile antenna users, the fractional energy efficiency constraint for fixed antenna users, and the secondary coupling between beam variables. To address this, this application first introduces auxiliary variables to equivalently or approximately rewrite the rate term, mobile antenna user SINR constraint, fixed antenna user energy efficiency constraint, and sensing performance constraint in the objective function. Subsequently, a continuous convex approximation method is used to locally convexize the non-convex terms. Finally, semidefinite relaxation transforms the beam vector optimization into a semidefinite programming problem.

[0090] Define semidefinite matrix variables

[0091] , ,

[0092] After introducing auxiliary variables and applying local convex approximation, the beamforming problem can be transformed into the following semidefinite summation programming approximation problem:

[0093]

[0094] This problem can be solved using standard convex optimization tools. However, since the rank-one constraint is not explicitly preserved after semidefinite relaxation, the optimal matrix solution generally does not satisfy...

[0095]

[0096] right and Gaussian randomization is used to recover the communication beam. The sensing beam matrix is ​​constructed using eigenvalue decomposition. Let denot...

[0097]

[0098] Then it can be derived from Construct a sensing beam matrix that satisfies the original constraints Finally, the recovered candidate beam solutions are substituted back into the original constraint set, and the feasible solution with the largest objective function value is selected as the beam update result for the current iteration.

[0099]

[0100] Step S2046, the sub-problem of optimizing the antenna position of the movable antenna user terminal.

[0101] After fixing the communication beam, sensing beam, and base station antenna positions, the optimization variable is only the set of movable antenna user-end antenna positions.

[0102]

[0103] Under this condition, the signal-to-interference-plus-noise ratio (SIR) of the m-th movable antenna user can be uniformly written as:

[0104]

[0105] in Represents the useful power term. This represents the sum of interference and noise terms.

[0106] Therefore, with the remaining variables fixed, the user-end antenna location optimization subproblem can be written as follows:

[0107]

[0108] The difficulty in solving this problem lies in and All of them change nonlinearly with the position variable, so the original fractional form is still nonconvex.

[0109] For ease of processing, auxiliary variables are introduced in the embodiments of this application. This indicates the achievable SINR level for the user at the current location.

[0110]

[0111] Therefore, the problem can be rewritten as

[0112]

[0113] because and Since it remains a non-convex function, this embodiment further constructs a local approximation for it at the current iteration point. The following convex approximation subproblem can be obtained.

[0114]

[0115] This problem can be solved efficiently in each inner iteration. After obtaining the new user antenna position, the linearized point is updated and the iteration continues until the subproblem converges.

[0116]

[0117] Step S2048, base station antenna position optimization sub-problem.

[0118] With the beamforming matrix fixed and the user-end antenna positions movable, the optimization variable is only the set of base station antenna positions.

[0119]

[0120] And satisfy the minimum spacing constraint

[0121]

[0122] Since the location of the base station antenna affects both types of communication users' effective channels and the equivalent sensing link of the sensing target, all three performance indicators can be uniformly written as... fractional form

[0123]

[0124] in and These represent the useful signal term and the interference term related to the antenna position, respectively. This allows the three types of performance indicators to be incorporated into a unified position optimization framework.

[0125] Therefore, the subproblem of optimizing the base station antenna position after fixing the beamforming matrix and the user-end antenna position of the movable antenna can be written as follows:

[0126]

[0127] Similar to the subproblem of optimizing the user terminal antenna position of a movable antenna, embodiments of this application further introduce auxiliary variables. This indicates the three types of SINR levels corresponding to the current antenna position. Then we have...

[0128]

[0129] Therefore, the problem can be rewritten as

[0130]

[0131] because and It is still a non-convex function, so we approximate it locally at the current iteration point. Let... and For the corresponding approximate function, we can further obtain...

[0132]

[0133] The problem is a convex approximation subproblem constructed at the current iteration point, which can be solved using standard convex optimization tools. After obtaining the new antenna position, the linearized point is updated and the iteration is repeated until the subproblem converges.

[0134]

[0135] This application verifies the effectiveness of the proposed joint optimization scheme through numerical simulation. The simulation scenario is set as a 500m×500m multi-base station integrated communication and sensing network, in which fixed antenna users, mobile antenna users, and aerial sensing targets coexist. During the physical transmission phase, each base station independently transmits communication and sensing signals based on the offline optimization results, without real-time baseband joint processing or coherent joint transmission. The network controller only acquires global CSI, user location, and sensing target location during the offline optimization phase to calculate the communication beam, sensing beam, and mobile antenna location configuration. The sensing part adopts a co-location sensing model, that is, each base station simultaneously undertakes the functions of sensing signal transmission and target echo reception.

[0136] To facilitate comparison of the impact of different optimization variables on system performance, the embodiments of this application select the following four schemes for comparison.

[0137] 1) JDT-RSB: Only the communication beam and sensing beam are optimized, while the antenna positions on both the user side and the base station side remain fixed. This scheme is used to characterize the baseline performance without antenna position optimization.

[0138] 2) FBOM: Jointly optimizes the position and beam variables of movable antennas on the base station side, while keeping the antenna position on the user side fixed. This scheme is used to analyze the contribution of movable antennas on the base station side to communication sensing performance.

[0139] 3) PBA: Jointly optimize the location and beam variables of movable antennas on the user side, while keeping the antenna location on the base station side fixed. This scheme is used to analyze the impact of movable antennas on the user side on system performance.

[0140] 4) Proposed: The joint optimization scheme proposed in the embodiments of this application simultaneously optimizes the communication beam, the sensing beam, the position of the user-side movable antenna, and the position of the base station-side movable antenna.

[0141] The four schemes differ in their optimizable degrees of freedom. JDT-RSB only has beam domain degrees of freedom, FBOM further introduces base station-side positional degrees of freedom, PBA further introduces user-side antenna positional degrees of freedom, while the Proposed scheme utilizes both base station-side and user-side spatial positional degrees of freedom. Therefore, by comparing these four schemes, the impact of different positional optimization modules on system communication and sensing performance can be observed more intuitively.

[0142] 1) Network topology settings

[0143] Figure 2 A schematic diagram of the network topology used in the embodiments of this application is provided. The simulation area is 500m × 500m, with base stations, fixed antenna users, mobile antenna users, and aerial sensing targets distributed in different spatial locations. Due to the significant differences in the distances between each communication user and sensing target and different base stations, the large-scale fading and small-scale fading conditions of different links are not the same. This spatial non-uniformity makes it difficult for the fixed antenna scheme to achieve optimal performance on all links simultaneously, and also provides practical significance for optimizing the location of mobile antennas.

[0144] The topology reveals that some users are close to the base station, enabling them to obtain strong communication links; while others are located between multiple base stations or at the edge of the area, making their link quality more susceptible to path loss and interference. In this situation, relying solely on beamforming for optimization limits the system's adjustable degrees of freedom. Introducing movable antennas allows the system to further improve channel conditions by adjusting the user-side receiver location or the base station-side array configuration, thereby enhancing the overall communication and sensing performance.

[0145] 2) The impact of transmit power on the system's communication sensing weighted sum rate

[0146] Figure 3 The changes in the system communication perception weighted sum and rate of four schemes under different base station transmit powers are presented. As the base station transmit power increases from 6W to 14W, the maximum sum and rate of all four schemes gradually increase. This trend is as expected, because higher transmit power can enhance the desired signal power and provide more power allocation space for beamforming design.

[0147] Under all transmit power values, the Proposed scheme consistently achieved the highest system communication-aware weighted sum rate. This indicates that simultaneously optimizing the positions of movable antennas on the user side, the movable antenna positions on the base station side, and beam variables can more fully utilize the spatial degrees of freedom in the system. In contrast, the JDT-RSB scheme can only adjust the beam direction under fixed array conditions, resulting in the lowest performance. This result demonstrates that when the antenna position is fixed, system performance is mainly limited by the fixed array response and given channel conditions, and relying solely on beamforming optimization is insufficient to further unlock spatial gain.

[0148] Both FBOM and PBA outperform JDT-RSB, indicating that introducing movable antennas on both the base station and user sides can improve system performance. However, as shown in the figure, PBA is generally higher than FBOM. This is mainly because movable antennas on the user side can directly change the channel conditions at the user's receiver, allowing the user's antenna to receive the desired signal in a more advantageous position, while reducing some interference, thus directly improving the communication rate. In contrast, while movable antennas on the base station side can also improve array response, their function requires serving multiple communication users and sensing targets simultaneously, and their optimization objective leans more towards a comprehensive trade-off, therefore the improvement in rate indicators is not as significant as that of PBA.

[0149] It is noteworthy that the proposed scheme maintains a stable advantage over other schemes as the transmit power increases. This indicates that the performance improvement of the proposed scheme is not a random result of a specific power point, but rather stems from the joint optimization structure itself. In other words, as power resources increase, joint position optimization and beam optimization can more effectively allocate the additional resources, thereby continuously achieving performance gains.

[0150] 3) The impact of the proportion of users with movable antennas on system performance

[0151] Figure 4 The proportion of users with movable antennas was further studied. Impact on system performance. To ensure fairness in comparisons across different user ratios, this embodiment keeps the total number of communication users constant, only changing the proportion of users with movable antennas on the user side. That is, as the proportion of users with movable antennas increases, the number of users with fixed antennas decreases accordingly. The left figure shows the maximum sum rate as... The results of the changes, shown in the right figure, are the average user rate of the movable antenna as a function of... The result of the change.

[0152] from Figure 4 As can be seen in the left image, with With increased mobility, both the reachability and speed of the PBA and Proposed schemes are significantly improved. This is because as the proportion of users with movable antennas increases, more communication links in the system can be improved through user-side antenna location optimization. For these users, the antenna location is no longer fixed but can be chosen in a given area to obtain a more favorable receiving location, thereby obtaining a stronger desired signal or a lower interference level. Therefore, the higher the proportion of movable antennas on the user side, the more degrees of positional freedom the system can utilize, and the overall communication performance is improved accordingly.

[0153] In contrast, the curves for JDT-RSB and FBOM remain essentially horizontal. This phenomenon is related to the optimization variable settings of the two schemes. JDT-RSB does not include any antenna position optimization, so its system performance naturally does not change significantly with the proportion of users with movable antennas. Although FBOM optimizes the antenna position on the base station side, it does not utilize the positional freedom of movable antennas on the user side, therefore, when... When changes occur, the available optimization dimensions do not change substantially, and the maximum sum rate of the system remains basically stable.

[0154] As can be seen from the right figure, the average user speed of a movable antenna does not increase with... Instead of increasing monotonically, the trend is not one of initial rise followed by a slight decline. This result has a certain degree of rationality. When When the number of mobile antenna users is small, increasing the number of users with movable antennas can significantly increase the system's degree of freedom in location optimization, allowing more users to benefit from receiver location adjustments, thus increasing the average data rate. As the number of mobile antenna users continues to increase, it also leads to stronger competition and interference coupling between users. Limited power and beam resources need to be allocated among more mobile users, and the average resources available to a single mobile antenna user are actually compressed. Therefore, at higher... Within the region, the average rate showed a slight decrease.

[0155] The proposed solution outperforms PBA in both figures, demonstrating that the movable antenna on the base station side not only improves sensing performance but also enhances multi-user communication environments by adjusting array configuration. User-side antenna location optimization primarily improves channel conditions for individual users at the receiver, while base station-side antenna location optimization simultaneously affects array responses in multiple user directions. Combining these two approaches allows the system to adjust at both the user receiving location and base station transmitting array levels, resulting in overall performance superior to the PBA solution, which only optimizes user-side location.

[0156] 4) The impact of the number of sensed targets on the worst-case sensing rate

[0157] Figure 5The worst-case target sensing rates for four schemes are presented under different numbers of sensing targets K. As K increases from 2 to 6, the worst-case sensing rates for all schemes decrease. This result is mainly due to two reasons. First, with limited total transmit power and antenna resources, increasing the number of sensing targets means that sensing beam resources are shared by more targets, relatively reducing the effective illumination energy that a single target can obtain. Second, echo coupling and interference between multiple sensing targets become more pronounced, making the sensing performance of the weakest target more susceptible to impact. Therefore, it is reasonable for the worst-case target sensing rate to decrease as K increases.

[0158] Comparing different schemes, the Proposed scheme achieved the highest worst-case sensing rate for all K values, indicating that joint optimization can effectively improve the system's ability to protect weakly sensed targets. Especially when the number of sensed targets increases, the system not only needs to improve the overall sensing performance but also needs to avoid some targets having excessively low performance. The Proposed scheme can flexibly change the array response in the sensing direction by simultaneously adjusting the antenna position and beam variables on the base station side, thereby improving the echo quality of the weakest target.

[0159] The FBOM (Flat Base Station Optimization) scheme significantly outperforms the PBA (Personal Base Station Optimization) scheme in sensing performance, a point worth noting. PBA primarily optimizes the location of movable antennas on the user side, but these user-side antennas do not participate in the transmission and reception of sensing signals; therefore, their direct impact on the sensing link is limited. FBOM, however, directly determines the configuration of the transmitting and receiving arrays of the sensing signal based on the location of the base station-side antennas, directly affecting energy focusing and echo reception in the direction of the sensing target. Therefore, in terms of worst-case sensing rate, optimization using movable antennas on the base station side is more advantageous.

[0160] This result also demonstrates that the roles of movable antennas on the base station side and the user side in the system are not entirely the same. Optimizing the location of user-side antennas is more focused on improving the receiving channel for communication users, while optimizing the location of base station-side antennas affects both the communication link and the sensing link, especially contributing more directly to sensing performance. Therefore, if the system only focuses on communication rate, movable antennas on the user side may bring more significant benefits; if the system needs to consider sensing performance as well, movable antennas on the base station side are a crucial factor that cannot be ignored.

[0161] 5) Comparison of performance components of different schemes

[0162] Figure 6 The composition of the total user rate for mobile antennas, the total user rate for fixed antennas, and the sensing rate were compared under four schemes. This figure provides a clearer view of the sources of performance improvement for different optimization schemes.

[0163] For the JDT-RSB scheme, since the antenna positions are all fixed, the system can only allocate resources under given channel conditions through beamforming, resulting in the lowest overall rate. The total rate of the MU, the total rate of the FU, and the sensing rate in this scheme are all limited by the fixed array structure, lacking the ability to further adjust the spatial channel.

[0164] The FBOM scheme achieves a higher total data rate than JDT-RSB. This is primarily because the optimized placement of the movable antennas on the base station side alters the spatial response of the base station array, improving both the fixed antenna user link and the sensing link. As shown in the bar chart, FBOM performs better in both total data rate (FU) and total sensing rate. This indicates that the movable antennas on the base station side not only improve the communication link but also enhance the effective array gain in the sensing direction.

[0165] The characteristics of the PBA scheme are even more pronounced. Because this scheme optimizes the location of movable antennas on the user side, users with movable antennas can choose better receiving positions, thus significantly improving the overall MU rate. However, PBA does not optimize the antenna array configuration on the base station side, so its improvement on sensing rate is relatively limited. This is consistent with the previous analysis of the worst-case sensing rate: user-side antenna location optimization mainly affects the communication receiver, rather than the sensing transmitter and echo receiver.

[0166] The proposed solution achieves the highest sum rate. While it may not necessarily outperform other solutions in every single component, it achieves a better overall balance between total MU rate, total FU rate, and sensing rate. The user-side movable antenna primarily improves the communication performance of users with movable antennas, while the base station-side movable antenna simultaneously improves communication user service and sensing link quality. The combination of the two results in a higher overall system benefit.

[0167] This figure further illustrates that the value of joint optimization goes beyond simply increasing a single type of rate; it involves redistributing spatial resources between communication and sensing, resulting in more coordinated performance across multiple metrics. This is particularly important for heterogeneous user sensing networks. The system includes mobile antenna users who can benefit from location adjustments, fixed antenna users whose locations are fixed and can only be improved through base station-side optimization, and airborne sensing targets that are sensitive to base station array responses. Optimizing the position of one antenna individually can only improve a portion of the link, while joint optimization can fully leverage the overall potential of mobile antennas.

[0168] This application addresses the integrated communication and sensing network of distributed multi-base station heterogeneous users assisted by movable antennas, studying the joint optimization problem of communication beams, sensing beams, user-side antenna positions, and base station-side antenna positions. Simulation results show that: First, increasing the transmit power can improve the system's maximum sum rate for all schemes, but the proposed scheme remains optimal at all power points, indicating that the joint optimization scheme has a stability advantage. Second, increasing the proportion of user-side movable antennas can improve system communication performance, but when the number of users with movable antennas is large, interference between users and resource competition will limit the continued growth of the average rate per user. Third, as the number of sensing targets increases, the worst sensing rate decreases, but optimization of base station-side movable antennas can effectively mitigate this performance loss. Fourth, the effects of optimization modules at different locations are significantly different. User-side movable antennas are more conducive to improving communication user rates, while base station-side movable antennas affect both sensing performance and communication user services. Joint optimization of both can achieve the optimal overall communication and sensing performance.

[0169] Therefore, the advantage of the proposed scheme does not come from a single optimization variable, but from the synergistic effect between beam design, user-side location adjustment, and base station-side array configuration optimization. This also illustrates that in multi-base station heterogeneous user communication and sensing integrated networks, relying solely on traditional beamforming or single-sided movable antenna optimization is insufficient to fully unleash the system's potential; joint design is a more effective solution.

[0170] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A joint resource optimization method for dual-sided movable antennas in a heterogeneous user integrated sensing network, characterized in that, include: The network status information of a multi-base station heterogeneous user communication and sensing integrated network is obtained. Based on the network status information, a communication channel and received signal model and a sensing target sensing signal model are established. Based on the communication channel and received signal model and the sensing target sensing signal model, communication performance indicators and sensing performance indicators are determined. Based on the communication performance indicators and the sensing performance indicators, the optimization objective is determined to be maximizing the total weighted rate of communication and sensing. Constraints are constructed by combining base station transmit power constraints, user communication service quality constraints, sensing target performance constraints, and antenna position constraints. Based on the aforementioned optimization objective and constraints, a joint resource optimization problem for dual-sided movable antennas is established, with communication beamforming variables, sensing beamforming variables, user-side movable antenna position variables, and base station-side movable antenna position variables as joint optimization variables. An alternating optimization method is used to iteratively optimize the joint resource optimization problem of the dual-sided movable antennas to obtain optimized joint optimization variables. Based on the optimized joint optimization variables, resource allocation is performed on the multi-base station heterogeneous user communication and sensing integrated network.

2. The method according to claim 1, characterized in that, Based on the network state information, a communication channel and received signal model and a sensing target sensing signal model are established. Based on the communication channel and received signal model and the sensing target sensing signal model, communication performance indicators and sensing performance indicators are determined, including: Based on the network status information, the location of the base station, the location of the communication user, the location of the sensing target, and the location of the antenna are obtained, and the communication channel between the base station and the communication user is determined according to the location of the base station, the location of the communication user, and the location of the antenna. Based on the communication channel and base station transmitted signal information, a communication channel and received signal model is constructed, which includes the desired communication signal, communication interference signal, perceived signal leakage interference, and received noise. Based on the location of the sensing target, the location information of the base station, and the signal information transmitted by the base station, the sensing link information between the base station and the sensing target is determined, and a sensing signal model of the sensing target is constructed based on the sensing link information. Communication performance indicators are determined based on the communication channel and received signal model, and sensing performance indicators are determined based on the sensing target sensing signal model.

3. The method according to claim 1, characterized in that, Based on the communication performance indicators and the sensing performance indicators, the optimization objective is determined to be maximizing the total weighted rate of communication and sensing. Constraints are constructed by combining base station transmit power constraints, user communication service quality constraints, sensing target performance constraints, and antenna location constraints, including: The communication performance evaluation result is determined based on the communication performance indicators, and the perception performance evaluation result is determined based on the perception performance indicators. Based on the communication performance evaluation results and the perception performance evaluation results, the optimization objective corresponding to the communication-perception weighted total rate is determined using the weighting parameters used to balance the contributions of the communication task and the perception task. Based on the communication performance indicators, the sensing performance indicators, and the preset network operating conditions, base station transmit power constraints, communication user service quality constraints, sensing target performance constraints, and antenna position constraints corresponding to the user-side movable antenna position and the base station-side movable antenna position are respectively constructed. Based on the optimization objective, the base station transmit power constraint, the communication user service quality constraint, the sensing target performance constraint, and the antenna location constraint, a problem constraint set for joint resource optimization is obtained.

4. The method according to claim 1, characterized in that, Based on the aforementioned optimization objective and constraints, a joint resource optimization problem for dual-sided movable antennas is established, with communication beamforming variables, sensing beamforming variables, user-side movable antenna location variables, and base station-side movable antenna location variables as joint optimization variables. This problem includes: Obtain the correlation between the communication beamforming variables, the sensing beamforming variables, the user-side movable antenna position variables, and the base station-side movable antenna position variables; Based on the aforementioned correlation, the communication beamforming variable and the sensing beamforming variable are used to adjust the transmission resources of the communication signal and the sensing signal, the user-side movable antenna position variable is used to adjust the spatial position of the movable antenna user receiver, and the base station-side movable antenna position variable is used to adjust the spatial configuration of the base station array. Based on the communication beamforming variables, the sensing beamforming variables, the user-side movable antenna position variables, and the base station-side movable antenna position variables, the optimization objective and constraints are jointly modeled to obtain the joint resource optimization problem of the dual-side movable antennas.

5. The method according to claim 1, characterized in that, The alternating optimization method is used to iteratively optimize the joint resource optimization problem of the dual-sided movable antenna, including: The position variables of the user-side movable antenna and the position variables of the base station-side movable antenna are fixed, and the communication beamforming variables and the sensing beamforming variables are jointly optimized according to the communication channel and sensing channel corresponding to the current antenna position. After updating the communication beamforming variables and the sensing beamforming variables, fix the updated communication beamforming variables, sensing beamforming variables, and the location variables of the movable antenna on the base station side, and optimize the location variables of the movable antenna on the user side. After completing the update of the user-side movable antenna position variables, the updated communication beamforming variables, sensing beamforming variables, and user-side movable antenna position variables are fixed, and the base station-side movable antenna position variables are optimized. The above optimization process is repeated until the change in the optimization objective corresponding to two adjacent iterations meets the convergence condition, thus obtaining the optimized joint optimization variables.

6. The method according to claim 5, characterized in that, Optimizing the position variables of the user-side movable antenna includes: The communication beamforming variable, the sensing beamforming variable, and the base station-side movable antenna position variable are fixed. Based on the mapping relationship between the user-side movable antenna position and the corresponding communication channel, the communication channels of different movable antenna users are updated. The impact of changes in the user-side movable antenna position on communication performance indicators is determined by combining the current communication beamforming result and the sensing beamforming result. Auxiliary variables are introduced to transform the non-convex performance constraints in the optimization process of the user-side movable antenna location; The user-side movable antenna position optimization problem is iteratively optimized based on the continuous convex approximation method, the user-side movable antenna position variables are updated, and the optimized user-side movable antenna position variables are obtained.

7. The method according to claim 5, characterized in that, Optimizing the position variables of the movable antenna on the base station side includes: The communication beamforming variable, the sensing beamforming variable, and the user-side movable antenna position variable are fixed, and the corresponding communication channel and sensing link response are determined based on the current position of the base station-side movable antenna. Based on the communication channel and the sensing link response, the influence relationship between the location variable of the movable antenna on the base station side and the communication performance index and the sensing performance index is determined, and a sub-problem of optimizing the location of the movable antenna on the base station side is constructed. For the non-convex performance expression and antenna position constraints in the sub-problem of optimizing the position of the movable antenna on the base station side, auxiliary variables are introduced to transform the communication performance index and the sensing performance index, and the continuous convex approximation method is used to perform local approximation processing on the non-convex part. Based on the base station-side movable antenna position optimization subproblem after local approximation processing, under the conditions of satisfying the feasible region constraint of the base station-side antenna position and the minimum spacing constraint between antennas, the position variables of the base station-side movable antenna are iteratively updated to obtain the optimized position variables of the base station-side movable antenna.

8. The method according to claim 5, characterized in that, Joint optimization of the communication beamforming variables and the sensing beamforming variables includes: By fixing the position variables of the user-side movable antenna and the position variables of the base station-side movable antenna, and based on the communication channel and sensing channel corresponding to the current antenna position, the optimization process corresponding to the communication beamforming variables and the sensing beamforming variables is transformed into a beamforming optimization sub-problem. For the non-convex objective function and non-convex constraints in the beamforming optimization subproblem, auxiliary variables are used to perform equivalent transformations on the communication performance index and the sensing performance index, and the non-convex part is locally convexized using the continuous convex approximation method. The semidefinite relaxation method is used to convert the quadratic matrix corresponding to the communication beamforming variable into a semidefinite matrix variable. The semidefinite programming problem after local convexity processing is solved to obtain the semidefinite matrix solution and the sensing covariance matrix solution corresponding to the communication beam. When the semidefinite matrix solution corresponding to the communication beam satisfies the rank-one condition, the communication beam shaping variables are recovered according to the principal eigenvector of the semidefinite matrix solution; when the semidefinite matrix solution does not satisfy the rank-one condition, the Gaussian randomization method is used to generate candidate solutions for the communication beam. Based on the eigenvalue decomposition of the solution of the sensing covariance matrix, sensing beamforming variables are constructed. The feasibility of the recovered communication beamforming variables and the constructed sensing beamforming variables is verified. The solution with the largest optimization objective value is selected from the candidate solutions that satisfy the original constraints and used as the updated communication beamforming variables and sensing beamforming variables.

9. The method according to claim 1, characterized in that, Based on the optimized joint optimization variables, resource allocation is performed on the multi-base station heterogeneous user communication sensing integrated network, including: Obtain the optimized communication beamforming variables, sensing beamforming variables, user-side movable antenna position variables, and base station-side movable antenna position variables; The optimized communication beamforming variables and the sensing beamforming variables are sent to the corresponding base station, so that the base station transmits communication signals and sensing signals respectively according to the communication beamforming variables and the sensing beamforming variables; The optimized user-side movable antenna position variable is sent to the corresponding movable antenna user, so that the movable antenna user adjusts the receiving antenna position according to the user-side movable antenna position variable; The optimized location variables of the base station-side movable antenna are sent to the corresponding base station, so that the base station adjusts the configuration of the base station-side movable antenna array according to the location variables of the base station-side movable antenna, thereby completing the resource configuration of the multi-base station heterogeneous user communication and sensing integrated network.

10. A dual-sided movable antenna joint resource optimization system for heterogeneous user integrated sensing networks, characterized in that, include: The model building module is configured to acquire network status information of a multi-base station heterogeneous user communication and sensing integrated network, establish a communication channel and received signal model and a sensing target sensing signal model based on the network status information, and determine communication performance indicators and sensing performance indicators based on the communication channel and received signal model and the sensing target sensing signal model. The constraint construction module is configured to determine, based on the communication performance index and the sensing performance index, the optimization objective is to maximize the total weighted rate of communication and sensing, and to construct constraint conditions by combining base station transmit power constraints, user communication service quality constraints, sensing target performance constraints and antenna position constraints. The determination module is configured to establish a joint resource optimization problem for dual-sided movable antennas based on the optimization objective and constraints, with communication beamforming variables, sensing beamforming variables, user-side movable antenna position variables, and base station-side movable antenna position variables as joint optimization variables; The optimization module is configured to iteratively optimize the joint resource optimization problem of the dual-sided movable antenna using an alternating optimization method to obtain optimized joint optimization variables, and to configure resources for the multi-base station heterogeneous user communication sensing integrated network based on the optimized joint optimization variables.

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