Interference suppression-oriented base station networking task allocation method in communication and inductance fusion network

Abstract

The invention discloses an interference suppression-oriented base station networking task allocation method in a communication sensing fusion network, which relates to the technical field of radars, and comprises the following steps of: firstly, carding receiving signal models of densely deployed base stations when executing a multi-target sensing task, and considering various interferences; and coupling a task allocation variable of the base station to calculation of a signal to interference plus noise ratio and a Bayesian Cramer-Rao bound, so as to represent the perception performance of a networking system on a target, and establishing a perception precision evaluation index. Then, in order to control the number of interference signals in networking, the base station networking task allocation problem is established as mixed integer programming which minimizes the number of started base stations under the condition that the preset sensing precision and the maximum sensing capacity of the base stations are met. And finally, designing a solving method based on an interior point method and greedy search to solve the generated optimization problem. The perception task allocation efficiency can be improved.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, specifically relating to a base station networking task allocation method for interference suppression in a sensor-communication fusion network. Background Technology

[0002] In a communication and sensing integrated network, when densely deployed base stations perform multi-target sensing tasks, their sensing signals will generate severe mutual interference, which will restrict the improvement of sensing accuracy.

[0003] Compared to traditional single-site sensing systems, cellular network architecture is essentially an interconnected sensing system. During sensing tasks, multiple base stations can collaborate to fuse multi-view information, eliminate blind spots, and improve positioning accuracy. However, when Integrated Sensing and Communication (ISAC) functionality is introduced into cellular networks, its classic multi-cell architecture introduces unprecedented challenges: interference becomes more complex. When multiple base stations simultaneously transmit sensing signals to track targets within their respective coverage areas, a base station's receiver receives not only the echo of its desired target but also sensing signals from neighboring base stations targeting other targets. This is known as cross-link interference, which severely contaminates the target echo signal—a problem absent in traditional point-to-point radar or single-site ISAC systems. Uncontrolled interference leads to decreased target detection accuracy, increased false alarm and false negative rates, weakened environmental awareness, and potentially affected the stability of multi-target sensing. Therefore, accurately identifying interference types and managing them accordingly can not only improve communication stability but also ensure the integrity and accuracy of sensed signals, thereby maximizing the advantages of the ISAC system. Currently, methods exist for ISAC systems that use orthogonal resource scheduling (time division, frequency division) to avoid interference between communication and sensing within the system; therefore, interference between sensed signals can be discussed separately.

[0004] However, the coverage requirements for radar sensing differ from those for communication in cellular base station networks. For communication, the signal travels directly from the base station to the user equipment, with path loss proportional to distance, forming a relatively uniform coverage circle centered on the base station. For radar sensing, the sensing signal travels from the base station to the target and then back to the base station via reflection. The path loss during this process is the product of two losses, and its sensing coverage depends on the sensing mode and the target's cross-section. This means the intensity of the sensing signal is inversely proportional to the fourth power of the distance, and its coverage is not a uniform circle. This leads to a critical problem: the sensing coverage is much smaller than the communication coverage, and its effective sensing area is uneven, often forming a discontinuous ring around the base station. Therefore, existing cellular network structures designed for communication purposes cannot meet the requirement of seamless sensing coverage, resulting in a loss of sensing accuracy.

[0005] Based on the above issues, when cellular base station networks achieve integrated sensing and communication, the traditional, isolated resource allocation methods are no longer applicable. It is necessary to treat interference management and meeting the requirements of sensing accuracy as a unified optimization problem and solve them collaboratively. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides a base station networking task allocation method for interference suppression in a sensor-communication fusion network. The technical problem to be solved by this invention is achieved through the following technical solution: This invention provides a base station networking task allocation method for interference suppression in a sensor-integrated network, comprising: Construct an integrated communication and sensing base station network, which includes multiple base stations and multiple targets. The base stations are distributed to form a cellular network structure, and the targets move in uniform linear motion. The base stations sense the targets in order to perform sensing tasks. In the In the secondary sensing task allocation, the working state vector of each base station and the sensing task allocation relationship matrix between each base station and each target are introduced to minimize the number of base stations in the base station network, construct the objective function for base stations to perform sensing tasks, and constrain the objective function. The working state vector of each base station is replaced by the first continuous variable, and the perception task allocation relationship matrix between each base station and each target is replaced by the second continuous variable. The objective function is relaxed into a continuous optimization problem, and the continuous optimization problem is solved to obtain the estimates of the first and second continuous variables. The estimates of the first and second continuous variables are projected using thresholds, and it is determined whether the constraints after projection are satisfied. If satisfied, the working state vector of each base station and the perception task allocation matrix between each base station and each target are output, and the next step is executed. If the task allocation is not satisfied, then adjust the task allocation using a greedy search. The next sensing task allocation is performed to reduce constraint violations until the global constraint violation count is zero or can no longer be reduced. The working state vectors of each base station and the sensing task allocation relationship matrix between each base station and each target are output. The next step is to execute the [further steps]. Secondary perception task allocation.

[0007] The beneficial effects of this invention are: This invention provides a base station networking task allocation method for interference suppression in a cellular base station network. In this method, the problem is modeled as a mixed integer programming problem with the objective of minimizing the number of active base stations, while simultaneously satisfying the signal-to-interference-plus-noise ratio thresholds for each target, the Bayesian Craméraud lower bound sensing accuracy requirements, and the limited sensing resources of each base station. The mixed integer programming problem is relaxed into a continuous optimization problem and solved. Then, feasibility improvements are made through projection and a global-perspective greedy search strategy. Ultimately, this method significantly improves solution efficiency while ensuring solution quality, and effectively guarantees target sensing accuracy.

[0008] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0009] Figure 1 This is a flowchart of a base station networking task allocation method for interference suppression in a sensor-integrated network provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a communication and sensing integrated base station network provided in an embodiment of the present invention; Figure 3 This refers to the number of base stations activated at each sensing moment, as provided in this embodiment of the invention. Figure 4 This is a schematic diagram of the minimum SINR among the base station combinations selected for each target at each sensing time, provided by an embodiment of the present invention, which satisfies the set threshold requirement of 10dB. Figure 5 This is a schematic diagram of the BCRLB of each target at each sensing moment provided by an embodiment of the present invention, satisfying a set threshold of 50m; Figure 6 This is a schematic diagram illustrating the number of targets simultaneously sensed by each base station at each sensing moment, provided in an embodiment of the present invention. Figure 7 This is a schematic diagram of a combination of base stations used to sense target 1 at each sensing moment, provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of a combination of base stations used to sense target 2 at each sensing moment, provided in an embodiment of the present invention; Figure 9This is a schematic diagram of a combination of base stations used to sense target 3 at each sensing moment, provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of a combination of base stations used to sense target 4 at each sensing moment, provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of a combination of base stations used to sense target 5 at each sensing moment, provided in an embodiment of the present invention; Figure 12 This is a schematic diagram of a combination of base stations used to sense target 6 at each sensing moment, provided in an embodiment of the present invention; Figure 13 This is a schematic diagram illustrating the changes in RMSE of each target before and after optimization at each sensing moment, provided by an embodiment of the present invention. Before optimization, no base station task allocation is performed, and all base stations sense the target. Figure 14 This is a schematic diagram comparing the time taken by the algorithm proposed in this invention to solve the optimization problem with that taken by the genetic algorithm. Detailed Implementation

[0010] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0011] Please see Figure 1 , Figure 1 This is a flowchart of a base station networking task allocation method for interference suppression in a sensor-integrated network provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of a communication and sensing integrated base station network provided in an embodiment of the present invention. The present invention provides a base station networking task allocation method for interference suppression in a communication and sensing fusion network, comprising: S101. Construct an integrated communication and sensing base station network, including multiple base stations and multiple targets. The base stations are distributed to form a cellular network structure, and each target moves in a uniform linear motion. The base stations sense the targets in order to perform sensing tasks.

[0012] Specifically, in this embodiment, the integrated communication and sensing base station network includes Each base station is distributed to form a cellular network structure. There are in the sensing area of ​​each base station Two point targets that are relatively far apart, and All are positive integers greater than 0.

[0013] make Indicates the first Secondary perception task allocation. , For a positive integer greater than 0, set the first... The timing of the secondary perception task allocation is , Indicates the time interval of perception.

[0014] It should be noted that the sensing task involves the base station transmitting a signal and receiving the echo signal after it passes the target, which means that the target has been sensed. The base station adopts a self-transmitting and self-receiving working mode.

[0015] Specifically, such as Figure 2 As shown, the location of base station 19 is set as the origin O. A Cartesian coordinate system is established with the north direction of base station 19 as the Y-axis and the east direction as the X-axis. The number can be 37, target The number can be 6, with 37 base stations forming a 4-layer cellular network structure. The number of base stations in each layer is 1, 6, 12, and 18, respectively. The value is 100.

[0016] Furthermore, the model for the target undergoing uniform linear motion is represented as follows: ; ; in, Indicate target In the The state vector in the sub-sensing task allocation. Indicate target In the The state vector in the sub-sensing task allocation. Represents the state transition matrix. Indicates zero mean and covariance as Gaussian noise, Indicate target In the The component of position in the X-axis direction in the secondary perception task assignment. Indicate target In the The component of velocity along the X-axis in the secondary perception task assignment. Indicate target In the The component of position in the Y-axis direction in the secondary sensing task assignment. Target In the The component of velocity in the Y-axis direction in the secondary sensing task allocation. Indicate target In the The component of position in the Z-axis direction in the secondary sensing task assignment. Indicate target In the The component of velocity in the Z-axis direction in the secondary sensing task allocation; base station For the target The measurement information received during sensing is represented as follows: ; in, Let be a nonlinear observation function, and we have: ; in, Indicate target In the Location information in the secondary sensing task allocation. Indicates base station Location information.

[0017] ; in, Indicates base station Received target Measurement information, Indicates base station Received target Distance information, Indicates base station Received target Angle information, The zero mean and covariance matrix is ​​represented as The Gaussian noise is not only affected by the base station's sensing task allocation results, but also related to fixed base station parameters, including signal bandwidth and beamwidth. In the In the secondary sensing task allocation, multiple base stations target the target. Measurements were performed, and a set of measurement information was constructed. Represented as: ; in, Indicates the first In the secondary perception task allocation, the participating targets The set of base stations for the sensing task. Indicates the index of the base station. Indicate participation goals The total number of base stations for sensing tasks. Indicates base station Participation Target The perception, Represents the empty set. Indicates the first In the secondary sensing task allocation, the base station With the goal The perception of task allocation relationships. Indicates the first In the secondary sensing task allocation, the base station Participation Target The perception.

[0018] Specifically, such as Figure 2 As shown, the initial states of the target are as follows: The initial state of objective 1 is: ; The initial state of objective 2 is: ; The initial state of objective 3 is: ; The initial state of objective 4 is: ; The initial state of objective 5 is:

[0019] The initial state of objective 6 is: ; in, This indicates the matrix transpose.

[0020] S102, in the In the secondary sensing task allocation, the working state vector of each base station and the sensing task allocation relationship matrix between each base station and each target are introduced to minimize the number of base stations in the base station network, construct the objective function for base stations to perform sensing tasks, and impose constraints on the objective function.

[0021] Specifically, in this embodiment, the operating state vector of each base station is represented as follows: ; in, Indicates the first The working state vector of each base station in the secondary sensing task allocation. This indicates the working status of each base station. Indicates the index of the base station. This indicates the total number of base stations. This indicates that the base station is in a turned-off state. This indicates that the base station is in the on state; The sensing task allocation relationship matrix between the base station and each target is represented as follows: ; in, Indicates the first The perception task allocation relationship matrix between the base station and each target in the secondary perception task allocation. Indicates the index of the target. This represents the total number of targets. Indicates the first In the secondary sensing task allocation, the base station With the goal The perception of task allocation relationships. Indicates the first In the secondary sensing task allocation, the base station Participation Target The perception, Indicates the first In the secondary sensing task allocation, the base station Not participating in the target The perception.

[0022] Furthermore, regarding the target According to the In the task assignment for the next perception, select to perceive this target. The signal-to-interference-plus-noise ratio (SINR) and Bayesian Craméro-Rao bound (BCRLB) of the base station combination are integrated to evaluate the target The accuracy of perception.

[0023] Specifically, in the In the secondary sensing task allocation, the base station network performs sensing tasks for targets within the coverage area, and the BCRLB of each target is related to the base station's working state vector. and task allocation matrix The function, at this time, the target The conditions that the set of state and measurement information must satisfy are expressed as follows: ; in, Indicates the target The mathematical expectation operation is performed on the set of state and measurement information.

[0024] Furthermore, regarding the target Perception accuracy Represented as: ; ; in, This represents the operation of finding the trace of a matrix. The inverse of the predicted Bayesian information matrix representing the target state is called the Cramérod bound. A predictive Bayesian information matrix that describes the target state.

[0025] The diagonal elements of the Bayesian Craméro bounds for each objective are usually considered as a lower bound for an objective state estimate, and are expressed as: ; in, Let represent the first element on the principal diagonal of the Bayesian Cramérod boundary, and let represent the error in the target position estimation on the X-axis. The third element on the principal diagonal of the Bayesian Cramérod boundary represents the error in the target position estimation along the Y-axis. The fifth element on the principal diagonal of the Bayesian Cramérod boundary represents the error in target position estimation along the Z-axis. By defining the above performance criteria, base station networking can adjust the working status and sensing task allocation of different base stations to change the target... The accuracy of perception.

[0026] Furthermore, according to the first In the secondary sensing task allocation, changes in the sensing task allocation for each base station will lead to changes in the sensing resources allocated by the base station to each target, causing significant fluctuations in the signal-to-interference-plus-noise ratio (SIR) and target sensing accuracy of the base station for each target. Therefore, the base station task allocation problem can be formulated as an integer optimization model; the objective function is: This means minimizing the number of base stations that are enabled in the network. This index is used to assess the number of interference signals present in the system. Furthermore, to ensure the system meets the required sensing accuracy for each target, a SINR threshold needs to be set. and the Bayesian Craméro boundary threshold Considering the limited sensing resources of base stations, it is necessary to set a maximum number of targets that a base station can simultaneously sense. This problem can be considered as a mixed integer programming problem.

[0027] Specifically, for target sensing tasks in a base station network, if all base stations sense the target, measurement information about the target can be obtained from multiple perspectives, thus achieving the best target sensing performance gain. However, using too many base stations for target sensing leads to a large number of sensing signals flooding the wireless environment within the coverage area. Limited by the limited sensing resources of a large-scale base station network system, mutual interference will occur between signals, thus deteriorating the base station's target detection. Therefore, if the task allocation relationship between all base stations and multi-target sensing can be optimized, and the base stations participating in target sensing can be used rationally, the system's task requirements for multi-target sensing and mutual interference suppression can be balanced, improving the overall system performance.

[0028] Therefore, considering the limited sensing resources of base stations, in order to minimize the impact of interference signals on sensing performance, a base station networking task allocation scheme is designed. This scheme reduces the number of active base stations while maintaining target sensing accuracy, thereby reducing the amount of interference signals in the system and optimizing the variable set. and The specific base station task allocation model is as follows: ; in, This represents the first constraint condition, for any base station. Received target Signal-to-interference-plus-noise ratio of the echo signal Greater than or equal to the first threshold This represents the second constraint condition for the objective. Perception accuracy Less than or equal to the second threshold , This represents the third constraint, which considers the limited resources sensed by each base station, and states that the number of targets sensed by each base station simultaneously is less than or equal to... , This indicates the fourth constraint: whether the base station detects the target depends on whether the base station is in an active state. This represents the fifth constraint, the base station. With the goal Perception of task allocation relationship It is a binary integer variable. This represents the sixth constraint, the base station. The working state is a binary integer variable, and the problem of solving the above objective function belongs to the mixed integer nonlinear programming (MINLP) problem.

[0029] For base stations Received target Signal-to-interference-plus-noise ratio of the echo signal The acquisition process is further explained; specifically: For a base station, the received signal is divided into three parts: valid echo signal, interference signal, and thermal noise. In the... In the secondary sensing task allocation, the base station The composition of each signal component is explained below: 1. Valid echo signal.

[0030] base station In response to a certain target When performing a perception task, the data received from the target echo signal For base stations In this context, the effective signal, which is the superposition of all target echo signals received by the base station, can be expressed as: ; in, This represents the radar cross section (RCS) of the target, and is assumed to be constant in this invention; For base stations The normalized complex envelope of the transmitted signal, This indicates the signal delay caused by the distance between the base station and the target. Indicates base station Received from target Echo signal power.

[0031] 2. Interference signals.

[0032] base station Signals received from other base stations are denoted as Consider two parts: scattering interference. and direct path interference Then the base station In the The interference signal received during the secondary sensing task allocation can be represented as the superposition of two interference components, namely: ; Scattering interference Defined as base station Signals illuminating the target may be scattered to the base station. The receiving end, therefore It can be represented as: ; in, Indicates signal from base station After launch, via the target After scattering, by the base station The latency of the entire reception process. Indicates base station Perceive target Afterwards, by the base station The power of the received scattered signal.

[0033] Direct path interference Defined as the sidelobes of signals from other base stations during sensing tasks that affect the base station. The resulting interference, therefore It can be represented as: ; in, Indicates base station With base station Signal delay caused by distance between them Indicates base station Received from base station The received signal power depends on the base station. The main lobe of the emitted signal is used to sense targets, including base stations. The interference is usually caused by the base station. The signal sidelobe.

[0034] 3. Thermal noise.

[0035] Thermal noise is defined as base station The noise generated by the receiver itself Assuming all base station antennas have the same parameters, their thermal noise power is: ,but satisfy: ; in, Represents the Boltzmann constant. This indicates the effective noise temperature of the base station receiver. Indicates receiver bandwidth. This represents the receiver noise figure.

[0036] Therefore, in the first In the secondary sensing task allocation, the base station In terms of the target When sensing, the SINR of the received echo signal is defined as: .

[0037] S103. Replace the working state vector of each base station with the first continuous variable, replace the sensing task allocation relationship matrix between each base station and each target with the second continuous variable, relax the objective function into a continuous optimization problem, and solve the continuous optimization problem to obtain the estimates of the first and second continuous variables.

[0038] Specifically, in this embodiment, a first continuous variable is used. Replace the working state vector of each base station , where the first continuous variable Represented as: ; in, express Relaxed variables; Using the second continuous variable Replace the perception task allocation matrix between the base station and each of the targets. ; where the second continuous variable Represented as: ; in, express Relaxed variables; Relaxing the objective function into a continuous optimization problem, it can be expressed as: ; in, Indicates base station In perceiving the target Effective echo signal power at that time Indicates thermal noise power. Indicates base station For base stations Direct path interference power, Indicates base station For base stations The scattering interference power, Represent a constant to ensure that in When that happens, the first constraint condition is automatically met.

[0039] The relaxed continuous optimization problem can be solved by an optimization method based on the interior point method and by introducing a barrier function to handle constraints, resulting in an optimized continuous base station task allocation matrix and base station working state vector.

[0040] It should be noted that for integer programming problems, traditional optimization techniques such as the cutting plane method and branch-and-bound (BB) are typically used. These algorithms usually rely on iterative processes to gradually converge to the optimal solution. However, this optimization problem has two complex nonlinear constraints, SINR and BCRLB, so a more effective strategy is needed to handle these two nonlinear constraints. The original optimization problem is relaxed into a continuous optimization problem. To do this, continuous variables are introduced. Replace base station working state vector Introducing continuous variable matrices Replacement of task allocation matrix Due to constraints Only for the perceived target The base station is valid, that is It is effective at this time, so a sufficiently large constant is introduced. This constraint is addressed by introducing a constant. After that, ensure At that time, the first constraint is automatically established. Since the time constraint is linear, solving the original optimization problem transforms into solving a continuous convex optimization problem.

[0041] S104. Project the estimates of the first continuous variable and the second continuous variable, and determine whether the constraints after projection are satisfied; if satisfied, output the working state vector of each base station, the perception task allocation matrix between each base station and each target, and execute the step... If the task allocation is not satisfied, then adjust the task allocation using a greedy search. The next sensing task allocation is performed to reduce constraint violations until the global constraint violation count is zero or can no longer be reduced. The working state vectors of each base station and the sensing task allocation relationship matrix between each base station and each target are output. The next step is to execute the [further steps]. Secondary perception task allocation.

[0042] Specifically, in this embodiment, based on the obtained estimate of the first continuous variable estimators of second continuous variables Projection is performed using a threshold of 0.5. Specifically, the projection is expressed as follows: ; After projection, check whether all constraints (SINR, BCRLB, and base station capacity) are satisfied. If all constraints are satisfied, output the operating state vector of each base station. Matrix of sensing task allocation between each base station and each target Execute the first If the task is assigned in the first perception phase, then proceed to the feasibility detection and greedy search phase.

[0043] Furthermore, by adjusting the greedy search... Sub-task allocation to reduce constraint violations until the global constraint violation count is zero or can no longer be reduced includes: For each objective Calculate the sum of the normalized violation levels of the SINR and BCRLB constraints, and use it as the global constraint violation quantity, expressed as: ; in, This indicates the degree of constraint violation in the current allocation scheme. Indicates the first threshold. Indicates the first In the secondary sensing task allocation, the base station Received target The signal-to-interference-plus-noise ratio of the echo signal, Indicates the first In the secondary sensing task allocation, the base station With the goal The perception of task allocation relationships. Indicates the index of the target. This represents the total number of targets. Indicates the index of the base station. This indicates the total number of base stations. Indicates the target The accuracy of perception. Indicates the second threshold; Statistics on base stations not involved in sensing ( ), and base stations that have not achieved the target of perception ( ), to perceive the target The task is assigned to the base station that does not participate in sensing. (i.e., setting) ), calculate the weighted index of global violation and change in objective function. , represented as: ; in, This indicates that a base station that does not participate in sensing is being activated. The reduction in the global violation amount afterward. This indicates the amount of base stations that have been activated. When the weighted index is maximized, the perception task is reallocated and updated. and ; Upgrade base stations Received target The signal-to-interference-plus-noise ratio of the echo signal, and the target Improve perception accuracy and update the set of targets that do not meet constraints. ; This process is repeated until the change in the weighted index after the perception task is reassigned is less than the third threshold. or target set When the set is empty, adjustments are made to complete the perception task.

[0044] Furthermore, it also includes: optimizing the objective function after the greedy search, including: If the base station Not involved in perceiving any target, that is ,set up Disable redundant base stations to optimize the objective function.

[0045] This process is repeated until the difference in the objective function between adjacent iterations is less than the fourth threshold. Or the number of iterations reaches the fifth threshold. Output the working state vector of each base station and the perception task allocation relationship matrix between each base station and each target, and execute the first step. Secondary perception task allocation.

[0046] In summary, this invention provides a subarray-level sparse optimization method for large-scale distributed arrays. In cellular base station networks, the problem is modeled as a mixed-integer programming problem with the objective of minimizing the number of active base stations, while simultaneously satisfying the signal-to-interference-plus-noise ratio (SIR) thresholds for each target, the Bayesian Craméraud lower bound for sensing accuracy, and the constraints of limited sensing resources for each base station. Furthermore, the SIR and Bayesian Craméraud lower bounds are derived considering the internal interference of sensing signals within the system. This invention proposes a two-stage optimization algorithm based on interior-point method and greedy search. This algorithm first obtains continuous approximate solutions to the problem through variable relaxation and interior-point method, then performs feasibility improvements through projection and a global-perspective greedy search strategy. Ultimately, it significantly improves solution efficiency while ensuring solution quality, and this method effectively guarantees target sensing accuracy.

[0047] In an optional embodiment of the present invention, the effectiveness of the base station networking task allocation method for interference suppression in the sensor-integrated network provided in the above embodiment is verified by simulation experiments, specifically as follows: I. Simulation Conditions like Figure 2 As shown, the base station networking system consists of The network consists of several base station nodes, forming a four-layer cellular network structure. Each layer has 1, 6, 12, and 18 base stations respectively. The distance between base stations is 520m. Each layer of base stations forms a regular hexagon. Each base station node has a bandwidth of 100MHz. The transmit and receive antenna gains of the base stations are set to 20dB, and the sidelobe antenna gain is set to -10dB. The transmit power of all base stations is [missing information]. The carrier frequency is .

[0048] Within the entire sensing area of ​​the system There are [number] targets, and their parameters are shown in Table 1. Furthermore, it is assumed that the RCS of all targets is [value]. And its height is always 500m.

[0049] Table 1 Initial parameter settings for each target

[0050] II. Simulation Content and Result Analysis This simulation was conducted based on 100 frames of observation data. In each frame, the SINR threshold was set to... BCRLB threshold is The maximum number of targets that a base station can simultaneously sense. The root mean square error (RMSE) is defined as follows: ; in, Indicates the first In the secondary perception task allocation, the target The true location Indicates the first The simulation, in the... In the secondary perception task allocation, the target The estimated location.

[0051] The simulation results above fully demonstrate the two advantages of the method proposed in this invention: 1. Real-time selection of base station networking based on the target's motion status can effectively reduce the number of base stations activated in the networking system. Please refer to [link to relevant documentation]. Figure 3 When multiple targets move toward the central area, base station reuse becomes more pronounced in order to reduce the number of base stations that need to be activated, thereby reducing the number of interference signals in the system.

[0052] 2. The solution proposed in this invention can meet the specific requirements for target perception accuracy and reduce perception error with fewer base stations.

[0053] The solution proposed in this invention can meet specific requirements for target perception accuracy with a smaller number of base stations. Please refer to [link / reference]. Figures 4-6 ; Figure 4 This is a schematic diagram illustrating how the minimum SINR among the base station combinations selected for each target at each sensing time, provided by an embodiment of the present invention, satisfies the set threshold requirement of 10dB. Figure 5 This is a schematic diagram illustrating how the BCRLB of each target at each sensing moment satisfies a set threshold of 50m, as provided in an embodiment of the present invention. Figure 6 This is a schematic diagram illustrating the number of targets simultaneously sensed by each base station at each sensing moment, as provided in an embodiment of the present invention. It demonstrates the limited sensing resources of each base station, which can sense up to 3 targets simultaneously. Figures 7-12 These are the specific base station allocation diagrams for each sensing moment from target 1 to target 6.

[0054] The proposed solution can reduce the perception error when sensing a target with fewer base stations. Please refer to [link / reference]. Figure 13 The RMSE of each target before and after optimization at each sensing moment is provided in the embodiments of the present invention. Before optimization, the sensing error is when all base stations participate in the sensing task of each target without base station task allocation. Compared with the baseline scheme, the proposed scheme of the present invention effectively reduces the number of base stations to be turned on and reduces the sensing error.

[0055] From the perspectives of sensing accuracy and the number of base stations activated, the method proposed in this invention has certain advantages. It also offers improvements in optimizing solution efficiency; please refer to [link to relevant documentation]. Figure 14The chart shows a comparison of the average solution time of the algorithm proposed in this invention for solving the optimization problem and the genetic algorithm for solving the same problem, as provided in an embodiment of this invention. The average solution time has been reduced.

[0056] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that an article or device comprising a list of elements includes not only those elements but also other elements not expressly listed. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device comprising said element. Terms such as "connected" or "linked" are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect. The orientations or positional relationships indicated by terms such as "upper," "lower," "left," and "right" are based on the orientations or positional relationships shown in the accompanying drawings and are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0057] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0058] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A base station networking task allocation method for interference suppression in a sensor-integrated network, characterized in that, include: A communication and sensing integrated base station network is constructed, including multiple base stations and multiple targets. The base stations are distributed to form a cellular network structure, and the targets move in uniform linear motion. The base stations sense the targets to perform sensing tasks. In the In the next sensing task allocation, the working state vector of each base station and the sensing task allocation relationship matrix between each base station and each target are introduced to minimize the number of base stations in the base station network, construct the objective function for the base station to perform sensing tasks, and constrain the objective function. The working state vector of each base station is replaced with a first continuous variable, and the perception task allocation relationship matrix between each base station and each target is replaced with a second continuous variable. The objective function is relaxed into a continuous optimization problem, and the continuous optimization problem is solved to obtain the estimates of the first and second continuous variables. The estimates of the first and second continuous variables are projected using a threshold, and it is determined whether the constraints after projection are satisfied. If satisfied, the working state vector of each base station and the perception task allocation relationship matrix between each base station and each target are output, and the first step is executed. If the task allocation is not satisfied, then adjust the task allocation using a greedy search. The next sensing task allocation is performed to reduce constraint violations until the global constraint violation count is zero or can no longer be reduced. The working state vectors of each base station and the sensing task allocation relationship matrix between each base station and each target are output. The next step is to execute the [further steps]. Secondary perception task allocation.

2. The base station networking task allocation method for interference suppression in a sensor-integrated network according to claim 1, characterized in that, The model representing the target's uniform linear motion is as follows: ； ； in, Indicate target In the The state vector in the sub-sensing task allocation. Indicate target In the The state vector in the sub-sensing task allocation. Represents the state transition matrix. Indicates zero mean and covariance as Gaussian noise, Indicate target In the The component of position in the X-axis direction in the secondary perception task assignment. Indicate target In the The component of velocity along the X-axis in the secondary perception task assignment. Indicate target In the The component of position in the Y-axis direction in the secondary sensing task assignment. Target In the The component of velocity in the Y-axis direction in the secondary sensing task allocation. Indicate target In the The component of position in the Z-axis direction in the secondary sensing task assignment. Indicate target In the The component of velocity in the Z-axis direction in the secondary sensing task allocation; The base station For the target The measurement information received during sensing is represented as follows: ； in, Represents a nonlinear observation function; in, Indicate target In the Location information in the secondary sensing task allocation. Indicates base station Location information; ； in, Indicates base station Received target Measurement information, Indicates base station Received target Distance information, Indicates base station Received target Angle information, The zero mean and covariance matrix is ​​represented as Gaussian noise; In the In the secondary sensing task allocation, multiple base stations target the target. Measurements were performed, and a set of measurement information was constructed. Represented as: ； in, Indicates the first In the secondary perception task allocation, the participating targets The set of base stations for the sensing task. Indicates the index of the base station. Indicate participation goals The total number of base stations for sensing tasks. To represent the empty set, Indicates the first In the secondary sensing task allocation, the base station With the goal The perception of task allocation relationships. Indicates the first In the secondary sensing task allocation, the base station Participation Target The perception.

3. The base station networking task allocation method for interference suppression in a sensor-integrated network according to claim 2, characterized in that, The operating state vector of each of the base stations is represented as follows: ； in, Indicates the first The working state vector of each base station in the secondary sensing task allocation. Indicates base station work status, Indicates the index of the base station. This indicates the total number of base stations. This indicates that the base station is in a turned-off state. This indicates that the base station is in the on state; The perception task allocation relationship matrix between the base station and each of the targets is represented as follows: ； in, Indicates the first The perception task allocation relationship matrix between the base station and each target in the secondary perception task allocation. Indicates the index of the target. This represents the total number of targets. Indicates the first In the secondary sensing task allocation, the base station With the goal The perception of task allocation relationships. Indicates the first In the secondary sensing task allocation, the base station Participation Target The perception, Indicates the first In the secondary sensing task allocation, the base station Not participating in the target The perception.

4. The base station networking task allocation method for interference suppression in a sensor-integrated network according to claim 3, characterized in that, The objective function and the constraints on the objective function are expressed as follows: ； in, This represents the first constraint condition, for any base station. Received target The signal-to-interference-plus-noise ratio of the echo signal Greater than or equal to the first threshold This represents the second constraint condition for the objective. Perception accuracy Less than or equal to the second threshold , This indicates the third constraint: the number of targets simultaneously sensed by each base station is less than or equal to... , This indicates the fourth constraint: whether the base station detects the target depends on whether the base station is in an active state. This represents the fifth constraint, the base station. With the goal Perception of task allocation relationship It is a binary integer variable. This represents the sixth constraint, the base station. Its working state is a binary integer variable.

5. The base station networking task allocation method for interference suppression in a sensor-integrated network according to claim 4, characterized in that, The target Perception accuracy Represented as: ； ； in, This represents the operation of finding the trace of a matrix. The inverse of the predicted Bayesian information matrix representing the target state is called the Cramérod bound. A predictive Bayesian information matrix that describes the target state.

6. The base station networking task allocation method for interference suppression in a sensor-integrated network according to claim 5, characterized in that, The target The conditions that the set of state and measurement information must satisfy are expressed as follows: ； in, Indicates the target The mathematical expectation operation is performed on the set of state and measurement information.

7. The base station networking task allocation method for interference suppression in a sensor-integrated network according to claim 4, characterized in that, The objective function is relaxed into a continuous optimization problem by replacing the operating state vector of each base station with a first continuous variable and replacing the perception task allocation relationship matrix between each base station and each target with a second continuous variable, thereby including: Using the first continuous variable Replace the operating state vector of each of the base stations , where the first continuous variable Represented as: ； in, express Relaxed variables; Using the second continuous variable Replace the perception task allocation matrix between the base station and each of the targets. ; where the second continuous variable Represented as: ； in, express Relaxed variables; Relaxing the objective function into a continuous optimization problem, it can be expressed as: ； in, Indicates base station In perceiving the target Effective echo signal power at that time Indicates thermal noise power. Indicates base station For base stations Direct path interference power, Indicates base station For base stations The scattering interference power, Represent a constant to ensure that in When that happens, the first constraint condition is automatically met.

8. The base station networking task allocation method for interference suppression in a sensor-integrated network according to claim 1, characterized in that, Adjust the first through greedy search Sub-perception task allocation to reduce constraint violations until the global constraint violation count is zero or can no longer be reduced, including: The global constraint violation is calculated and expressed as: ； in, This indicates the degree of constraint violation in the current allocation scheme. Indicates the first threshold. Indicates the first In the secondary sensing task allocation, the base station Received target The signal-to-interference-plus-noise ratio of the echo signal, Indicates the first In the secondary sensing task allocation, the base station With the goal The perception of task allocation relationships. Indicates the index of the target. This represents the total number of targets. Indicates the index of the base station. This indicates the total number of base stations. Indicates the target The accuracy of perception. Indicates the second threshold; The statistics include base stations that do not participate in sensing and base stations that have not reached the sensing target. The task is assigned to the base station that does not participate in sensing. Calculate the weighted index of global violation and change in objective function. , represented as: ； in, This indicates that a base station that does not participate in sensing is being activated. The reduction in the global violation amount afterward. This indicates the amount of base stations that have been activated. When the weighted index is maximized, the perception task is reallocated and updated. and ; Upgrade base stations Received target The signal-to-interference-plus-noise ratio of the echo signal, and the target Improve perception accuracy and update the set of targets that do not meet constraints. ; This process is repeated until the change in the weighted index after the perception task is reassigned is less than the third threshold. or target set When the set is empty, adjustments are made to complete the perception task.

9. The base station networking task allocation method for interference suppression in a sensor-integrated network according to claim 8, characterized in that, Also includes: After the greedy search, the objective function is optimized, including: If the base station Not involved in perceiving any target, that is ,set up The redundant base stations are shut down to optimize the objective function.

10. The base station networking task allocation method for interference suppression in a sensor-integrated network according to claim 9, characterized in that, The optimization of the objective function includes: During adjacent iterations, the difference in the objective function is less than the fourth threshold. Or the number of iterations reaches the fifth threshold. .

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