Low-altitude scene-oriented dynamic sensing task scheduling optimization method

Abstract

The invention provides a dynamic sensing task scheduling optimization method for a low-altitude scene, which introduces a dynamic parameter adjustment mechanism driven by a sensing demand in a sensing dimension, and can effectively solve the problem of unbalanced communication and sensing demands in an actual ISAC system by evaluating the sensing demand in real time and intelligently configuring the sensing parameters, thereby improving the real-time performance of the system. The flexibility of system design is improved; in the communication dimension, an intra-frame multi-user matching mechanism is constructed, and compared with an existing low-efficiency scheduling strategy that only a single user occupies a single time slot resource alone, the method allows multiple users to share the single time slot resource at the same time; the problem of interference accumulation in a multi-user scene is remarkably improved by dynamically optimizing a matched user set in the frame while the communication efficiency is guaranteed; besides, the mechanism can dynamically adjust the frame-user matching strategy according to the channel state change between the unmanned aerial vehicle and the user, so that the communication spectrum efficiency is maximized, and the deviation between the calculation preference and the real performance income is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of Integrated Sensing and Communication (ISAC), and more specifically, to an optimization method for Dynamic ISAC Task Scheduling (DITS) for low-altitude scenarios. Background Art

[0002] With the large-scale deployment of 5G / 6G networks, the ISAC technology has significantly improved the utilization rate of spectrum, power, and hardware resources by deeply integrating sensing and communication functions, and has become one of the core technologies for building the next-generation intelligent wireless network. However, the existing ground ISAC systems face technical bottlenecks in actual deployment - their system performance highly depends on the Line-of-Sight (LOS) propagation path between the transceiver ends. In complex urban scenarios, non-line-of-sight (NLOS) environments such as building blockages will not only cause a significant decrease in sensing accuracy, but also force the system to increase the transmission power to maintain the link service quality, thus bringing problems such as interference complexity, seriously restricting the overall performance of the system. To address the above technical problems, the Unmanned Aerial Vehicle (UAV) platform, with its unique mobility, provides a new technical path to break through the performance bottleneck of traditional ground ISAC systems.

[0003] The existing research on UAV-assisted ISAC systems mainly focuses on the optimal resource allocation of radar sensing and communication and UAV positioning. In the literature "Joint maneuver and beamforming design for UAV-enabled integrated sensing and communication", Lyu et al. proposed a beamforming optimization scheme based on Successive Convex Approximation (SCA) and Semi-Definite Relaxation (SDR) techniques, and designed an alternating optimization mechanism to calculate the deployment trajectory of UAVs. The literature "Trajectory design and power control for joint radar and communication enabled multi-UAV cooperative detection systems" studied the joint optimization problem of UAV trajectory and power allocation for multi-UAV-assisted ISAC systems, and proposed a mechanism based on Deep Reinforcement Learning (DRL) to achieve the trade-off optimization between radar sensing and communication performance. The literature "Constrained utility maximization in dual-functional radar-communication multi-UAV networks" proposed a multi-UAV cooperative ISAC system and designed a three-stage iterative scheme, which can jointly optimize UAV-user association, UAV positioning and power allocation, and ensure a certain sensing accuracy while maximizing the system communication rate. In addition, the literature "On the interplay between sensing and communications for UAV trajectory design" discussed the real-time trajectory design problem of each time slot in the ISAC system, and proposed a hybrid optimization mechanism combining Extended Kalman Filter (EKF) and SCA technology to achieve the comprehensive improvement of communication and sensing performance. However, although the above schemes improve the system performance through joint optimization of power and UAV trajectory in static task allocation scenarios, they generally do not consider the dynamic communication and sensing task requirements during actual system deployment. Therefore, there are still problems such as difficulty in coping with the asymmetric conflicts in the priority and resource occupancy of communication and sensing tasks.

[0004] To solve the above problems, it is necessary to consider constructing a dynamic ISAC task-time resource matching framework. Therefore, it is particularly important to consider the impact of ISAC task scheduling on the performance of the ISAC system. Currently, the relevant work discussing ISAC task scheduling mainly focuses on the frame structure of Time Division Multiplexed ISAC (TDM-ISAC). For example, the literature "Throughput maximization for UAV-enabled integrated periodic sensing and communication" realizes a more flexible trade-off between communication and sensing by jointly optimizing ISAC beams, sensing time selection, frame-user association, etc. However, limited by the inherent characteristic that the TDM-ISAC frame structure can only support single-target sensing and single-user communication in each time slot, it is difficult to break through the performance bottleneck in communication and sensing efficiency. In contrast, the Cooperative ISAC (Co-ISAC) frame structure allows simultaneous execution of multi-target sensing and multi-user communication, and has significant advantages in terms of communication and sensing efficiency.

[0005] However, existing research on the Co-ISAC frame structure mostly focuses on waveform design and lacks in-depth exploration of dynamic sensing and communication task scheduling. For example, the traditional Static Frame Resource Configuration (SFRC) method provided in the literature "UAV-enabled integrated sensing and communication: Opportunities and challenges" requires all frames to perform the same communication and sensing tasks, that is, all time slots within the entire task window need to simultaneously meet the requirements of the same full-target sensing and full-user communication; this mode has the following defects: firstly, it ignores the asymmetric requirements of sensing and communication in the actual system; secondly, there are strict sensing and communication requirements in all time slots, which greatly limits the flexibility of system design. Summary of the Invention

[0006] To overcome the defects of fixed frame structure, rigid resource allocation, and insufficient multi-task cooperation efficiency in the existing Co-ISAC technology, the present invention provides a dynamic communication and sensing task scheduling optimization method for low-altitude scenarios. For the sensing task scheduling problem therein, the present invention dynamically adjusts the sensing time slots based on the Adaptive ISAC (AISAC) method, and under the condition of a given sensing time slot configuration, adopts an innovative Delayed User Matching (DUM) method based on a delay strategy to implement the scheduling of communication tasks; while ensuring the communication and sensing efficiency, the present invention can more flexibly allocate communication and sensing task resources according to the actual sensing and communication requirements, achieving better system performance.

[0007] To solve the above technical problems, the technical solution of the present invention is as follows:

[0008] A dynamic communication and sensing task scheduling optimization method for low-altitude scenarios, comprising the following steps:

[0009] S1: Build a communication and sensing integrated system including at least one unmanned aerial vehicle (UAV), multiple ground users, and multiple ground sensing targets. Among them, the UAV serves as an aerial access point to provide downlink communication services for ground users, and at the same time performs radar sensing on ground sensing targets;

[0010] Divide the task window of the communication and sensing integrated system into several frames, each frame serving a different set of ground users; further divide each frame into several time slots, each time slot for separately executing a communication task, or separately executing a sensing task, or simultaneously executing a communication and a sensing task;

[0011] S2: Build a communication model of the communication and sensing integrated system, calculate the communication spectral efficiency between the UAV and each ground user according to the communication model, and estimate the Cramér-Rao bound of the departure angle between the UAV and each ground sensing target;

[0012] S3: Dynamically configure the sensing task scheduling factor of each time slot of the communication and sensing integrated system based on the Adaptive ISAC algorithm to complete the dynamic scheduling of sensing tasks;

[0013] S4: Based on the configured sensing task scheduling factor and the Cramér-Rao bound of the departure angle between the UAV and each ground sensing target, introduce sensing constraints, and with the goal of maximizing the communication spectral efficiency between the UAV and each ground user, construct and solve the beamforming optimization problem corresponding to each frame to obtain the beam optimization result of each frame;

[0014] S5: Based on the beam optimization results corresponding to each frame, construct a frame-multi-user matching problem; restrict to match only a single user each time, and adopt a deferred acceptance strategy with a preference update mechanism to iteratively solve the frame-multi-user matching problem, obtaining the matching results between each frame and different sets of ground users, and completing the dynamic scheduling of communication tasks.

[0015] Preferably, in step S1, the integrated communication and sensing system includes at least one unmanned aerial vehicle (UAV), K single-antenna ground users, and J ground sensing targets; the UAV is configured with a uniform linear array of M antennas and flies from a preset initial position to a target position within a mission period;

[0016] Use the UAV as an aerial access point to provide downlink communication services for ground users and simultaneously perform radar sensing on ground targets;

[0017] Represent the index sets of ground users and ground sensing targets as and Denote the planar position of ground user as u k =(u k,x , u k,y ), and denote the planar position of ground sensing target as v j =(v j,x , v j,y );

[0018] Decompose the mission period into multiple consecutive mission windows, and optimize the entire mission period by optimizing each mission window; the duration of each mission window is T and contains frames, where T C is the frame length, and the frame index is denoted as Further divide each frame into time slots, where τ is the duration of each time slot, and the time slot index is denoted as Each mission window contains a total of N = C·N C time slots, and use (c, n) to represent the time slot indices of different frames in each mission window;

[0019] Denote the planar position of the UAV as q[c, n]=(q x [c, n], q y [c, n]), where q x [c, n] and q y [c, n] are the X-axis and Y-axis coordinates of the UAV in time slot (c, n) respectively; assume the UAV flies at a fixed height H that meets air traffic control regulations;

[0020] The UAV transmits a signal matrix in time slot (c, n) where \(L\) is the length of the signal frame; the signal matrix \(X[c,n]\) satisfies \(X[c,n]=W[c,n]S[c,n]\), where contains the data streams sent to \(K\) ground users. It is assumed that the data streams are independent of each other, that is (·) H is the conjugate transpose operator; is the beamforming matrix, which is used to simultaneously realize communication and sensing functions.

[0021] Preferably, in the step S1, the sensing task scheduling factor \(o[c,n]\in\{0,1\}\) and the communication task scheduling factor \(z\) k [c,n]\(\in\{0,1\}\) of the integrated communication and sensing system are defined, which are respectively used to represent whether to perform the sensing task and whether to provide communication services for the ground user \(k\) in the time slot \((c,n)\). Among them, \(o[c,n]=1\) means that the sensing task is performed in the time slot \((c,n)\), and \(o[c,n]=0\) means that the sensing task is not performed in the time slot \((c,n)\); \(z\) k [c,n]=1 means that communication services are provided for the user \(k\) in the time slot \((c,n)\), and \(z\) k [c,n]=0 means that there is no communication with the user \(k\) in the time slot \((c,n)\);

[0022] At the same time, the following constraint conditions are set for the integrated communication and sensing system:

[0023] At least one communication or sensing task must be performed in each time slot to ensure the full utilization of the system's time resources, that is, it satisfies:

[0024] During the entire task window \(T\), any ground user communicates with the UAV at least once, that is, it satisfies:

[0025] Given the upper limit \(Q\) of the number of ground users communicating in each time slot, and \(Q\geq K / C\), that is, it satisfies:

[0026] Preferably, in the step S2, the communication model of the integrated communication and sensing system includes: a communication received signal sub-model and a sensing received signal sub-model;

[0027] The communication received signal sub-model is constructed based on the LOS channel model, assuming that the Doppler effect caused by the movement of the UAV has been fully compensated at the ground user end; the channel vector \(h\) of the UAV to the ground user \(k\) in the time slot \((c,n)\) k is expressed as:

[0028]

[0029] where \(\beta_0\) represents the channel power gain corresponding to the reference distance \(d_0\), denotes the distance between the UAV and the ground user k, a k [c,n] denotes the antenna steering vector pointing to the ground user k, satisfying d is the antenna spacing, λ is the wavelength, θ k [c,n] is the departure angle between the UAV and the ground user k, (·) T is the transpose operator;

[0030] The signal y received by the ground user k in the time slot (c,n) k [c,n] is expressed as:

[0031]

[0032] where is the desired signal, represents the interference between users, represents the additive Gaussian white noise received by the ground user k, is the noise power;

[0033] The communication spectral efficiency R of the ground user k in the time slot (c,n) k [c,n] is expressed as:

[0034]

[0035] The described sensing received signal sub-model includes:

[0036] Assume that the Doppler frequency shift caused by the ground sensing target and the UAV movement has been fully compensated, and the ground sensing target is modeled as an unstructured point target. Then, the sensing channel G of the ground sensing target j j [c,n] is expressed as:

[0037]

[0038] where is the complex reflection coefficient, ∈ j represents the radar cross section of the ground sensing target j, e j [c,n] is the distance between the UAV and the ground sensing target j; in a monostatic radar setup, θ j [c,n] is the departure angle between the UAV and the ground sensing target j;

[0039] Estimate the departure angle θ between the UAV and each ground sensing target j The Cramér-Rao bound Πs(θ j [c,n]) of [c,n], is expressed as:

[0040]

[0041] Among them, represents A j [c,n]| θ the derivative of θ j with respect to [c,n], is the perceived noise level.

[0042] Preferably, in step S3, define the parameter change rate f i s at the i-th perception, which is used to dynamically adjust the sensing task scheduling factor o[c,n], expressed as:

[0043]

[0044] Among them, is the preset key parameter at the i-th perception; the value of f i S should be proportional to the parameter change rate within adjacent sensing periods;

[0045] Dynamically configure the sensing task scheduling factors for each time slot of the integrated communication and sensing system based on the adaptive ISAC algorithm, including the following steps:

[0046] S3.1: Initialize i = 1, and initialize the parameters sensing interval Δ i , maximum sensing interval Δ max and minimum sensing interval Δ min ;

[0047] S3.2: Calculate the parameter change rate f i S at the i-th perception, compare the size of f i S with the preset threshold . If then increase the sensing interval Δ i at the (i + 1)-th perception, expressed as Δ i = min{Δ i + 1, Δ max}; if then decrease the sensing interval Δ i at the (i + 1)-th perception, expressed as Δ i = max{Δ i - 1, Δ min}; where min{·} represents taking the minimum value in the set, and max{·} represents taking the maximum value in the set;

[0048] S3.3: Repeatedly iterate step S3.2 until the sensing intervals for the entire task window are obtained, then terminate the iteration, and configure the sensing task scheduling factors o[c,n] for each time slot according to the obtained sensing intervals.

[0049] Preferably, in the step S4, the beamforming optimization problem is specifically:

[0050]

[0051] where represents the set of ground users served within the c-th frame; η k represents the weight coefficient of the ground user k, and the larger η k , the higher the priority of the ground user k in spectrum efficiency optimization; the constraint C1 is the sensing constraint, indicating that the value of the Cramer-Rao bound is less than a given threshold ξ to ensure that the sensing performance meets the preset requirements; the constraint C2 indicates that the transmission power of the UAV cannot exceed the maximum power P max ;

[0052] Solve the beamforming optimization problem for each frame to obtain the optimal beam vector corresponding to each frame Take the optimal beam vector as the beam optimization result.

[0053] Preferably, in the step S5, the frame-multiuser matching problem is specifically:

[0054]

[0055] where is the optimal solution obtained by solving the problem P1 under the given Γ c , and the objective function value corresponding to , |Γ c | represents the number of ground users served within the c-th frame.

[0056] Preferably, in the step S5, the frame-multiuser matching problem is iteratively solved according to the following steps:

[0057] S5.1: Initialization: Initialize all ground users and time slots as unmatched, and set the reservation list for each frame Calculate the preference list F between each frame and ground users based on the following formula c,k :

[0058]

[0059] S5.2: Update the reservation list: Each ground user sends a request to the most preferred frame c according to the preference list F obtained by initialization c,k and the time slot *Send a matching request; after each frame receives the matching request from the ground user, screen the requesting user according to its preference list. When multiple requests are received simultaneously, only select the top B preferred ground users into the reserved list and reject other ground users, where B is a preset positive integer;

[0060] S5.3: One-to-one matching: The rejected ground user k sends a matching request to the next optimal frame that can be matched according to the current preference list; after each frame receives the request from the new ground user, perform the following matching decision in combination with the current reserved list: first, select the most preferred ground user k from the union of the request set of the new ground user and the current reserved list * Complete the final matching; then update the next B preferred users to the current reserved list, and reject the requests of all other ground users after the update;

[0061] S5.4: Matching and preference update: After the matching is completed, add the ground user k * to the set Γ c , and exclude it in subsequent matchings; if a certain frame reaches its capacity limit, mark this frame as unavailable and exclude it in subsequent matchings;

[0062] S5.5: The rejected ground users dynamically adjust their preference lists according to the latest matching results, and repeat steps S5.3 to S5.4 until all ground users complete the matching, and output the final set Γ c .

[0063] Preferably, construct the following problem:

[0064]

[0065] where, where and respectively represent the set of rejected users and the set of frames that have not reached the capacity limit; is a binary variable. When the ground user k is the most preferred request of the c-th frame in the i-th iteration, otherwise Constraint C9 limits that each ground user can be matched with at most one frame; Constraint C10 limits that each frame can select at most one ground user for matching during a single iteration;

[0066] Solve problem P3. In step S5.2, update the reserved list according to the solution result of problem P3; in step S5.3, update the request set of the new ground user according to the solution result of problem P3.

[0067] Preferably, solve problem P1 based on the successive convex approximation algorithm; solve problem P3 based on the Hungarian algorithm.

[0068] Compared with the prior art, the beneficial effects of the technical solution of the present invention are as follows:

[0069] The present invention provides a dynamic communication and sensing task scheduling optimization method for low-altitude scenarios. On the one hand, a DITS mechanism for Co-ISAC frame structure is proposed. Compared with traditional communication scheduling strategies that only focus on resource competition and interference management among communication users, the DITS mechanism integrates sensing task scheduling into resource allocation decisions, realizing the collaborative scheduling of sensing tasks and multi-user communication.

[0070] In the sensing dimension, a dynamic parameter adjustment mechanism driven by sensing requirements is introduced. The DITS mechanism can effectively solve the problem of unbalanced communication and sensing requirements in actual ISAC systems and improve the flexibility of system design by evaluating sensing requirements in real time and intelligently configuring sensing parameters. In the communication dimension, an intra-frame multi-user matching mechanism is constructed. Compared with existing inefficient scheduling strategies that only support single-user exclusive use of single-slot resources, the DITS mechanism allows multiple users to share single-slot resources simultaneously. While ensuring communication efficiency, it significantly improves the problem of interference accumulation in multi-user scenarios by dynamically optimizing the set of matching users within the frame.

[0071] In addition, the present invention also designs an inter-frame matching user set switching strategy, which can dynamically adjust the frame-user matching strategy according to the channel state changes between UAVs and users, thereby maximizing the communication spectrum efficiency. The user matching optimization methods in traditional communication only consider the communication channel quality and ignore the sensing task requirements, making it difficult to suppress the cross-domain interference between communication and sensing signals. The DUM method fully utilizes the design hierarchy between beam optimization and frame-multi-user matching, and incorporates the sensing task requirements as constraints into the user matching decision-making process, thereby effectively suppressing the cross-domain interference between communication and sensing signals.

[0072] The DUM method adopts a multi-round iteration strategy to complete the matching during the matching process, and effectively avoids the risk of falling into a local optimal solution due to premature acceptance of a sub-optimal matching by using the Deferred Acceptance (DA) strategy. Compared with the traditional DA algorithm, the DUM method provided by the present invention effectively reduces the deviation between the calculated preference and the actual performance gain by dynamically adjusting the user preference list and restricting the number of users in a single match. Description of the Drawings

[0073] Figure 1 It is a flowchart of a dynamic communication and sensing task scheduling optimization method for low-altitude scenarios provided in Embodiment 1.

[0074] Figure 2 It is a block diagram of the processing flow of the DITS mechanism provided in Embodiment 2.

[0075] Figure 3It is a schematic diagram of the frame structure and allocation under the DITS mechanism provided in Embodiment 2.

[0076] Figure 4 It is a curve graph showing the variation of the average spectral efficiency with the number of users provided in Embodiment 3.

[0077] Figure 5 It is a curve graph showing the variation of the communication and sensing performance with the number of antennas provided in Embodiment 3.

[0078] Figure 6 It is a trend graph showing the variation of the communication and sensing performance with the CRB threshold provided in Embodiment 3. Detailed implementation manners

[0079] The accompanying drawings are only for illustrative purposes and should not be construed as limitations to this application;

[0080] To better illustrate this embodiment, some components in the accompanying drawings are omitted, enlarged or reduced, which do not represent the dimensions of the actual product;

[0081] For those skilled in the art, it is understandable that some well-known structures and their descriptions in the accompanying drawings may be omitted.

[0082] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0083] Embodiment 1

[0084] As Figure 1 shown, this embodiment provides a dynamic communication and sensing task scheduling optimization method for low-altitude scenarios, including the following steps:

[0085] S1: Build a communication and sensing integrated system including at least one unmanned aerial vehicle (UAV), multiple ground users, and multiple ground sensing targets. Among them, the UAV serves as an aerial access point to provide downlink communication services for ground users, and at the same time performs radar sensing on ground sensing targets;

[0086] Divide the task window of the communication and sensing integrated system into several frames, each frame is used to serve different sets of ground users; further divide each frame into several time slots, and each time slot is used to execute a communication task alone, or a sensing task alone, or both a communication and a sensing task simultaneously;

[0087] S2: Build a communication model of the communication and sensing integrated system, calculate the communication spectral efficiency between the UAV and each ground user according to the communication model, and estimate the Cramer-Rao bound of the departure angle between the UAV and each ground sensing target;

[0088] S3: Dynamically configure the sensing task scheduling factor of each time slot of the communication and sensing integrated system based on the adaptive ISAC algorithm to complete the dynamic scheduling of sensing tasks;

[0089] S4: Based on the configured sensing task scheduling factor and the Cramér-Rao bound of the departure angle between the UAV and each ground sensing target, introduce sensing constraints, and aim to maximize the communication spectral efficiency between the UAV and each ground user. Construct and solve the beamforming optimization problem corresponding to each frame to obtain the beam optimization results for each frame;

[0090] S5: Based on the beam optimization results corresponding to each frame, construct a frame-multiuser matching problem; restrict to match only a single user each time, and adopt the deferred acceptance strategy with a preference update mechanism to iteratively solve the frame-multiuser matching problem, obtaining the matching results between each frame and different ground user sets, and completing the dynamic scheduling of communication tasks.

[0091] In the specific implementation process, the application scenario of this method is a typical ISAC scenario based on UAVs. First, build a communication and sensing integrated system including at least one UAV, multiple ground users, and multiple ground sensing targets. Among them, the UAV serves as an air access point to provide downlink communication services for ground users, and at the same time performs radar sensing on ground sensing targets;

[0092] Divide the task window of the communication and sensing integrated system into several frames, and each frame is used to serve different ground user sets; further divide each frame into several time slots, and each time slot is used to separately execute communication tasks, or separately execute sensing tasks, or simultaneously execute communication and sensing tasks;

[0093] Then construct the communication model of the communication and sensing integrated system, calculate the communication spectral efficiency between the UAV and each ground user according to the communication model, and estimate the Cramér-Rao bound of the departure angle between the UAV and each ground sensing target;

[0094] After that, dynamically configure the sensing task scheduling factor for each time slot of the communication and sensing integrated system based on the adaptive ISAC algorithm to complete the dynamic scheduling of sensing tasks;

[0095] Based on the configured sensing task scheduling factor and the Cramér-Rao bound of the departure angle between the UAV and each ground sensing target, introduce sensing constraints, and aim to maximize the communication spectral efficiency between the UAV and each ground user. Construct and solve the beamforming optimization problem corresponding to each frame to obtain the beam optimization results for each frame;

[0096] Finally, based on the beam optimization results corresponding to each frame, construct a frame-multiuser matching problem; restrict to match only a single user each time, and adopt the deferred acceptance strategy with a preference update mechanism to iteratively solve the frame-multiuser matching problem, obtaining the matching results between each frame and different ground user sets, and completing the dynamic scheduling of communication tasks;

[0097] This method addresses the problem of sensing task scheduling. It dynamically adjusts the sensing time slots based on the adaptive ISAC method and, under the condition of a given sensing time slot configuration, uses an innovative Delay-based User Matching (DUM) method to achieve the scheduling of communication tasks. While ensuring the efficiency of communication and sensing, this method can more flexibly allocate communication and sensing task resources according to actual sensing and communication requirements, achieving better system performance.

[0098] Embodiment 2

[0099] As Figure 2 shown, this embodiment provides a dynamic communication and sensing task scheduling optimization method for low-altitude scenarios, including the following steps:

[0100] S1: Build an integrated communication and sensing system including at least one unmanned aerial vehicle (UAV), multiple ground users, and multiple ground sensing targets. Among them, the UAV serves as an aerial access point to provide downlink communication services for ground users and simultaneously performs radar sensing on ground sensing targets.

[0101] Divide the task window of the integrated communication and sensing system into several frames, each frame serving a different set of ground users. Further divide each frame into several time slots, each time slot for separately performing a communication task, or a sensing task, or simultaneously performing a communication and a sensing task.

[0102] S2: Build a communication model for the integrated communication and sensing system, calculate the communication spectral efficiency between the UAV and each ground user according to the communication model, and estimate the Cramér-Rao bound of the departure angle between the UAV and each ground sensing target.

[0103] S3: Dynamically configure the sensing task scheduling factor for each time slot of the integrated communication and sensing system based on the adaptive ISAC algorithm to complete the dynamic scheduling of sensing tasks.

[0104] S4: Based on the configured sensing task scheduling factor and the Cramér-Rao bound of the departure angle between the UAV and each ground sensing target, introduce sensing constraints, and with the goal of maximizing the communication spectral efficiency between the UAV and each ground user, construct and solve the beamforming optimization problem corresponding to each frame to obtain the beam optimization result for each frame.

[0105] S5: Based on the beam optimization result corresponding to each frame, construct a frame-multiuser matching problem. Limit matching only one user at a time, and use the deferred acceptance strategy with a preference update mechanism to iteratively solve the frame-multiuser matching problem to obtain the matching result between each frame and different sets of ground users, completing the dynamic scheduling of communication tasks.

[0106] In the specific implementation process, the application scenario of this method is a typical ISAC scenario based on UAVs. The following elaborates on the technical solution in detail;

[0107] First, a joint communication and sensing system is established. The joint communication and sensing system includes at least one unmanned aerial vehicle (UAV), K single-antenna ground users, and J ground sensing targets. The UAV is equipped with a uniform linear array (ULA) with M antennas and flies from a preset initial position to a target position within a mission cycle. The UAV is used as an aerial access point to provide downlink communication services to ground users and simultaneously perform radar sensing on ground targets. The index sets of ground users and ground sensing targets are respectively denoted as and The planar position of ground user is denoted as u k =(u k,x , u k,y ), which can be obtained through uplink signal estimation or the Global Positioning System (GPS). The planar position of ground sensing target is denoted as v j =(v j,x , v j,y ), and its value is usually determined by a specific sensing task. For example, v j can be set as the uniform sampling position of the region of interest in a target detection task or the estimated position of the previous frame in a target tracking task;

[0108] For the convenience of system design, in this embodiment, the mission cycle is divided into multiple consecutive mission windows, and the optimization of the entire mission cycle is achieved by optimizing each mission window. For the convenience of description, the following conducts system modeling, scheme design, and simulation verification for a single mission window;

[0109] The duration of each mission window is T, which contains frames, where T C is the frame length, and the frame index is denoted as Each frame is further divided into time slots, where τ is the duration of each time slot, and the time slot index is denoted as Each mission window contains a total of N = C·N C time slots, and (c, n) is used to represent the time slot indices of different frames in each mission window;

[0110] The planar position of the UAV is denoted as q[c, n]=(q x [c, n], q y [c, n]), where q x [c, n] and q y [c, n] are the X-axis and Y-axis coordinates of the UAV in the time slot (c, n), respectively. It is assumed that the UAV flies at a fixed height H that meets air traffic control regulations;

[0111] At the transmitting end, the UAV transmits the signal matrix in time slot (c, n). where L is the length of the signal frame; the signal matrix X[c, n] satisfies X[c, n]=W[c, n]S[c, n], where contains the data streams sent to K ground users. Assuming that the data streams are independent of each other, i.e., is the conjugate transpose operator; is the beamforming matrix, which is used to simultaneously realize communication and sensing functions;

[0112] Next, a communication model of the integrated communication and sensing system is constructed, including: a communication received signal sub-model and a sensing received signal sub-model;

[0113] 1) Communication received signal sub-model:

[0114] Since there is generally a strong LOS path in the A2G scenario, in this embodiment, a communication received signal sub-model is constructed based on the LOS channel model, assuming that the Doppler effect caused by the movement of the UAV has been fully compensated at the ground user end; the channel vector h k [c, n] from the UAV to the ground user k in time slot (c, n) is expressed as:

[0115]

[0116] where β0 represents the channel power gain corresponding to the reference distance d0, represents the distance between the UAV and the ground user k, and a k [c, n] represents the antenna steering vector pointing to the ground user k, satisfying d is the antenna spacing, λ is the wavelength, and θ k [c, n] is the departure angle between the UAV and the ground user k, and (·) T is the transpose operator;

[0117] The signal y k [c, n] received by the ground user k in time slot (c, n) is expressed as:

[0118]

[0119] where is the desired signal, represents the interference between users, represents the additive white Gaussian noise (AWGN) received by the ground user k, is the noise power;

[0120] The communication spectral efficiency R of the ground user k in time slot (c, n) k[c,n] is expressed as:

[0121]

[0122] 2) Perceived received signal sub-model:

[0123] For the radar perception task of the target in the task area, assuming that the Doppler frequency shift caused by the ground perception target and the UAV movement has been fully compensated, and the ground perception target is modeled as an unstructured point target, then the perception channel G of the ground perception target j j [c,n] is expressed as:

[0124]

[0125] Where, is the complex reflection coefficient, ∈ j represents the radar reflection surface of the ground perception target j, e j [c,n] is the distance between the UAV and the ground perception target j; in the case of a monostatic radar setup, θ j [c,n] is the departure angle between the UAV and the ground perception target j;

[0126] In this embodiment, the Cramér-Rao Bound (CRB) is used as the evaluation index for the target estimation performance. The CRB provides a theoretical lower bound for the mean squared error (MSE) of the parameter estimator and is an important index for measuring the perception performance; for the point target scenario, when the directions of other targets are given, estimating the departure angle θ j [c,n] between the UAV and each ground perception target, the Cramér-Rao bound Πs(θ j [c,n]) is expressed as:

[0127]

[0128] Where, represents A j [c,n]| θ the derivative of θ j [c,n], is the size of the perception noise;

[0129] Based on the above system model, the design goal of this method is to maximize the system communication performance by designing a dynamic communication-perception task scheduling mechanism and a frame-multiuser intelligent matching strategy under the premise of meeting the perception performance constraints;

[0130] Such as Figure 3As shown in the figure, the DITS mechanism proposed in this embodiment realizes the efficient integration and application of communication and sensing tasks through sensing and communication task scheduling. First, for the sensing task scheduling problem, the sensing time slots are dynamically adjusted based on the AISAC method. And under the condition of a given sensing time slot configuration, the innovative DUM method is adopted to realize the scheduling of communication tasks.

[0131] 1) For the sensing task, this method proposes a demand-based dynamic sensing task allocation mechanism, that is, according to the accuracy requirements of the actual sensing task and the dynamic changes of the application scenario, flexibly adjust the allocation of the number of sensing time slots, so as to achieve the optimal balance between the utilization efficiency of time resources and sensing performance. Specifically, when high-precision sensing tasks need to be executed, the number of sensing time slots can be increased to improve the sensing time and sensing frequency. On the contrary, the number of sensing time slots can be reduced, and more time resources can be allocated to communication tasks, thereby improving the overall time resource utilization efficiency of the system.

[0132] 2) For the communication task, a dynamic scheduling mechanism supporting intra-frame multi-user matching is proposed. Specifically, by establishing a frame-multi-user dynamic matching relationship, a specific subset of users is selected as the communication task object within each ISAC frame duration. Through this mechanism, both the spectrum efficiency gain brought by multi-user time resource sharing is maintained, and the multi-user interference and the interference between communication and sensing tasks are suppressed by optimizing the best service user set. Secondly, an inter-frame user set switching strategy is also designed. Based on the large-scale channel changes caused by the movement of the UAV, the frame-user matching relationship is dynamically adjusted, thereby further optimizing the spectrum efficiency of the system. In addition, from the perspective of simplifying system design and ensuring user fairness, it is assumed that there is no overlap between the subsets of users matched in each frame within the same task window, but any user has the right to resource allocation in different task windows.

[0133] To further quantify the time resources of the system, this embodiment also defines the sensing task scheduling factor o[c,n] ∈ {0,1} and the communication task scheduling factor z k [c,n] ∈ {0,1} of the integrated communication and sensing system, which are respectively used to represent whether the sensing task is executed and whether communication services are provided to the ground user k in the time slot (c,n). Among them, o[c,n] = 1 means that the sensing task is executed in the time slot (c,n), and o[c,n] = 0 means that the sensing task is not executed in the time slot (c,n); z k [c,n] = 1 means that communication services are provided to the user k in the time slot (c,n), and z k [c,n] = 0 means that there is no communication with the user k in the time slot (c,n).

[0134] To better optimize the system task scheduling, the following constraint conditions are set for the integrated communication and sensing system:

[0135] Each time slot must perform at least one communication or sensing task to ensure the full utilization of the system's time resources, that is, it satisfies:

[0136]

[0137] Within the entire task window T, any ground user communicates with the UAV at least once, that is, it satisfies:

[0138]

[0139] Considering the limited load capacity of the UAV, the upper limit Q of the number of ground users communicating in each time slot is given, and Q ≥ K / C, that is, it satisfies:

[0140]

[0141] In summary, the core strategy of the DITS mechanism proposed in this embodiment is: based on the actual system requirements and environmental dynamic parameters, optimally select the binary decision variables {o[c,n]} and {z k [c,n]}; the solution process of this decision variable will be elaborated in detail below;

[0142] AISAC:

[0143] In this embodiment, based on the adaptive ISAC algorithm (AISAC), the sensing task scheduling factor {o[c,n]} of each time slot of the integrated communication and sensing system is dynamically configured to achieve the optimal balance between sensing requirements and time resource allocation;

[0144] Specifically, define the parameter change rate f at the i-th sensing i S , which is used to dynamically adjust the sensing task scheduling factor o[c,n], expressed as:

[0145]

[0146] Among them, is the preset key parameter at the i-th sensing, such as indicators such as the target moving speed and the mean square error of position estimation. These parameters can be obtained through real-time sensing by the UAV; f i S should be proportional to the parameter change rate in the adjacent sensing period; for example, when the target moving speed increases or the position estimation MSE increases, f i S increases accordingly. At this time, a longer sensing duration needs to be configured to prevent the target from being lost or reduce the positioning error; conversely, when f i S is small, the sensing duration can be appropriately shortened to optimize the utilization rate of time resources;

[0147] Dynamically configure the sensing task scheduling factor for each time slot based on the adaptive ISAC algorithm, including the following steps:

[0148] a) Initialize i = 1 and initialize the parameters Sensing interval Δ i 、maximum sensing interval Δ max and minimum sensing interval Δ min ; Δ max is used to ensure that the sensing interval does not exceed the upper limit tolerable by the system to avoid abnormal situations such as target loss; Δ min is used to prevent the continuous decrease of the sensing interval;

[0149] b) Calculate the parameter change rate f i S at the i-th sensing, compare the size of f i S with the preset threshold . If , increase the sensing interval Δ i at the (i + 1)-th sensing, which is expressed as Δ i = min{Δ i + 1, Δ max}; if , decrease the sensing interval Δ i at the (i + 1)-th sensing, which is expressed as Δ i = max{Δ i - 1, Δ min}; where, min{·} represents taking the minimum value in the set, and max{·} represents taking the maximum value in the set;

[0150] c) Repeat step b) iteratively until the sensing intervals for the entire task window are obtained, then terminate the iteration, and configure the sensing task scheduling factor o[c, n] for each time slot according to the obtained sensing intervals;

[0151] DUM:

[0152] Under the condition of a given sensing factor configuration, this embodiment realizes frame - multi - user matching in the DITS mechanism based on the proposed DUM method to achieve communication task scheduling. Different from the user matching optimization scheme in traditional communication that only considers interference between users, the DUM method incorporates the sensing task requirements as potential constraint conditions into the user matching decision - making process by utilizing the hierarchical coupling relationship between beam optimization and frame - multi - user matching, thus effectively reducing multi - user interference and communication - sensing interference. Compared with traditional user matching methods, this method can achieve obvious advantages in both communication performance and sensing performance. The technical details will be elaborated in detail below;

[0153] Since the user sets matched in each frame do not overlap with each other, that is, there is no cross-frame interference among the users served by different frames, the beam optimization problem can be decomposed into independent sub-problems based on each frame and its served user set; specifically, the beam optimization can be further divided into C independent sub-problems, and each sub-problem is associated with the communication users served within the corresponding frame; to quantify the frame-multiuser matching problem, a set is defined in this embodiment, which represents the user set served within the c-th frame; therefore, the frame-multiuser matching can be characterized by the set Γ c . When , the problem of beam optimization can be expressed as:

[0154]

[0155] where: η k represents the weight coefficient of different users, and the larger its value, the higher the priority of user k in spectrum efficiency optimization; the constraint C1 indicates that when performing the sensing task, the CRB value needs to be less than the given threshold ξ to ensure that the sensing performance meets the preset requirements; the constraint C2 means that the transmit power of the UAV cannot exceed the maximum power P max ; Solving equation (10) can be based on existing beam optimization methods, such as the SCA scheme;

[0156] Based on the optimal beam obtained by solving equation (10) , the frame-multiuser matching problem can be expressed as:

[0157]

[0158] where: is the optimal solution obtained by solving problem P1 under the given Γ c , the corresponding objective function value, |Γ c | represents the number of users served within the c-th ISAC frame; the constraints C3-5 clarify the key limitations in the communication and sensing task scheduling, which are equivalent to equations (6)-(8); equation (11) reveals the hierarchical dependence relationship between the frame-multiuser matching problem and beam optimization, that is, the frame-user matching is an optimization problem based on the beam optimization result;

[0159] To effectively solve problem P2, the proposed DUM method based on matching theory will be elaborated in detail below; first, define the matching where and the matching Τ(x) needs to satisfy the following conditions simultaneously:

[0160]

[0161] Based on the above constraint (14), the above many-to-many matching problem can be simplified to a one-to-many matching problem; to solve the simplified matching problem, the preference relationship between the elements to be matched is further defined; for the c-th ISAC frame, the preference of any user k can be quantified as: the performance gain in spectral efficiency obtained when providing communication services for it during the duration of this frame compared to not providing services; the specific definition is as follows:

[0162]

[0163] Based on the above definition of preference, it can be known that when and only when does user k prefer to match with the c1-th ISAC frame; similarly, when and only when does the UAV prefer to provide communication services for user k1 during the c-th frame duration;

[0164] The core challenge of the above matching problem lies in the dynamics and competitiveness during the pairing process, which may lead to a deviation between the preference calculated based on Equation (15) and the true performance gain; the deviation mainly stems from the following two key factors:

[0165] 1) Users may dynamically adjust the ISAC frames they match during the iteration process, resulting in instability of the preference relationship;

[0166] 2) Multiple users may compete to match the same ISAC frame simultaneously, triggering conflicts in resource allocation;

[0167] To effectively reduce the deviation between the estimated performance gain and the true value, the following constraint mechanism is introduced in this embodiment:

[0168] 1) During the matching process, each user maintains a stable match with the initially matched ISAC frame, thus avoiding frequent re-pairing;

[0169] 2) It is specified that each ISAC frame can only complete a match with one user in a single iteration;

[0170] In addition, to reduce the risk of falling into a local optimal solution due to prematurely accepting a sub-optimal match, this solution refers to the idea of retaining the optimal candidate in the DA method on the premise of satisfying the above constraints, and designs a multi-stage iterative scheme based on this to solve the above matching problem; compared with the traditional DA method, the DUM method proposed in this embodiment effectively reduces the deviation between the calculated preference and the true performance gain caused by the competitiveness among users by restricting only one user to be matched each time; in addition, different from the static preference list used in the DA method, a preference update mechanism is introduced in the DUM method, which can adaptively adjust the user preference according to the real-time matching result, enabling the preference calculation to more accurately reflect the true system performance gain; the specific implementation process is as follows:

[0171] a) Initialization: Set the reservation list of each ISAC frame Calculate the preference list F of each ISAC frame and user based on Equation (15) c,k ; Initialize all users and time slots as unmatched, that is, define the set of rejected users and the set of unmatched frames

[0172] b) Update the reservation list: Each user sends a matching request to the most preferred ISAC frame according to the pre-constructed preference list, that is, request a match After each ISAC frame receives a user request, it screens the requesting users according to its preference list. When receiving multiple requests, only select the most preferred B users to enter the reservation list and reject other users, that is, update the reservation list where the superscript (B) represents the top B most preferred users;

[0173] c) One-to-one matching: The rejected user k sends a matching request to the next available optimal ISAC frame according to the current preference list, that is, request a match where c * represents the ISAC frame requested by the user in the previous round; After each ISAC frame receives a new user request, it performs the following matching decision in combination with the current reservation list: First, select the most preferred user from the request set and the reservation list to complete the final matching, where Γ c ′ is the serial number of the currently most preferred requesting user; Second, update the next preferred B users to the reservation list, that is, Ψ c = Ψ c ∪{Γ c ′}\{k*}; Finally, reject the remaining requesting users;

[0174] d) Matching and preference update: After the matching is completed, update the matched set Γ c = Γ c ∪k*, update the set of rejected users If the ISAC frame reaches the capacity limit, that is, |Γ c | = Q, then the corresponding frame is marked as unavailable, and at this time, the set of unmatched frames needs to be updated Finally, the rejected users dynamically adjust their preference lists according to the latest matching results;

[0175] e) Convergence and termination: Repeat the matching update process of steps c) and d) until all users are matched, and output the optimal matching result;

[0176] The core part of the above iterative scheme is the one-to-one matching process in step c); based on the acceleration matching idea in the prior art, the user request - acceptance process in step c) can be equivalently transformed into solving the optimization problem P3, that is, directly obtaining the current most preferred requesting user Γ c ′ through solving P3; this transformation enables the matching relationships between multiple users and ISAC frames to be processed simultaneously in each iteration, thus effectively reducing the maximum number of iterations of the scheme; specifically, in the i-th iteration, the problem P3 can be expressed as:

[0177]

[0178] where and c u respectively represent the set of rejected users and the set of frames that have not reached the capacity limit; the constraint C8 defines as a binary variable. When user k is the most preferred request for the c-th ISAC frame in the i-th iteration, otherwise The constraint C9 restricts that each user can be matched with at most one ISAC frame; the constraint C10 restricts that each ISAC frame can select at most one user for matching in a single iteration process;

[0179] The problem P3 is a classic bipartite graph matching problem and can be solved based on the Hungarian algorithm; in addition, the update process of the reserved list in step b) of the above iterative method can also be solved using Equation (16);

[0180] Based on the above analysis, the iterative process of the DUM method proposed in this embodiment is summarized in Table 1:

[0181] Table 1 Iterative Process of DUM Method

[0182]

[0183]

[0184] This method aims at the sensing task scheduling problem, dynamically adjusts the sensing time slots based on the adaptive ISAC method, and under the condition of a given sensing time slot configuration, adopts an innovative user matching based on delay strategy (DUM) method to realize the scheduling of communication tasks; while ensuring the communication and sensing efficiency, this method can more flexibly allocate communication and sensing task resources according to the actual sensing and communication requirements, and achieve better system performance.

[0185] Embodiment 3

[0186] This embodiment provides a simulation experiment to verify the effectiveness of a dynamic communication and sensing task scheduling optimization method for low-altitude scenarios proposed in Embodiment 2.

[0187] In the specific implementation process, the simulation parameters of the integrated communication and sensing system constructed in this embodiment are shown in Table 2:

[0188] Table 2 System Simulation Parameter Table

[0189]

[0190]

[0191] In addition to selecting SFRC as the comparison scheme for the DITS mechanism in this embodiment, the comparison scheme of the DUM method is constructed by replacing the frame-multiuser matching method in the proposed DITS mechanism with the Maximum Cut (MC) method and the Kmeans method; to ensure the fairness of the comparison, the same beamforming and trajectory optimization methods are used for all schemes;

[0192] First, the effectiveness of the DUM method proposed in Embodiment 2 is verified; Figure 4 shows the comparison of the average spectral efficiency of different methods in the equal-power beam, Straight Flight (SF) scenario; the average spectral efficiency is defined as: The simulation results show that as the number of users increases, the average spectral efficiency gradually decreases; this is mainly due to the limitation of the total transmit power and the increase of interference between users; in addition, by comparing the performance of different schemes, it can be found that the performance of the DITS mechanism is not always better than the SFRC method, and its advantage only appears under specific user matching schemes, which further verifies that the DUM method proposed in Embodiment 2 can be used to achieve effective dynamic communication and sensing task scheduling;

[0193] Next, Figure 5 the relationship between the communication and sensing performance and the number of antennas during joint trajectory and beam optimization is analyzed; among them, Figure 5 in (a) shows the communication performance characterized by the relationship between the spectral efficiency and the number of antennas, Figure 5 in (b) shows the sensing performance characterized by the average root CRB of AoD estimation under different numbers of antennas; for the convenience of description, the root CRB is denoted as RCRB AoD , and its definition is Analysis Figure 5 From the simulation results in, the following conclusions can be drawn: (1) The spectral efficiency increases with the increase of the number of antennas, and the RCRB AoD decreases with the increase of the number of antennas, which indicates that more antennas provide higher beamforming gain in communication and sensing tasks; (2) Due to its flexible resource allocation mechanism, the DITS mechanism has better performance in terms of spectral efficiency and RCRB AoDThey are all superior to the SFRC method in terms of indicators. Especially in the scenario of a high number of antennas, the advantage in spectral efficiency is more significant; (3) The RCRB AoD decreases as the number of antennas increases, and the downward trend gradually flattens out, indicating that the sensing performance may be limited by other factors such as noise at this time; (4) By comparison Figure 4 it can be found that when jointly optimizing the ISAC beam and the UAV trajectory, the MC and Kmeans schemes can achieve higher spectral efficiency compared to the SFRC method, which shows that they can obtain the gain brought by the flexibility of system design with the help of the DITS mechanism; (5) In all test scenarios, the DUM method proposed in Embodiment 2 achieves the optimal communication and sensing performance;

[0194] Finally, Figure 6 the trade-off relationship between communication and sensing performance is discussed; Figure 6 In (a), the relationship between spectral efficiency and the CRB threshold is shown. As the threshold ζ increases, the average spectral efficiency shows an upward trend. At this time, the sensing requirement decreases, and more power resources are allocated to support communication; In addition, the simulation results show that the DUM method proposed in Embodiment 2 achieves the highest spectral efficiency, fully verifying the superiority of the proposed scheme; Figure 6 In (b), the relationship between sensing performance and the CRB threshold is discussed. It can be found that the RCRB AoD has the same change trend as the threshold change, that is, the system can achieve better sensing performance under higher sensing requirements, indicating that the system has the ability to dynamically adjust the resource allocation strategy according to different sensing requirements.

[0195] The same or similar reference numerals correspond to the same or similar components;

[0196] The terms describing the positional relationship in the drawings are only for illustrative purposes and cannot be construed as a limitation to this application;

[0197] Obviously, the above embodiments of the present invention are only examples for clearly explaining the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A method for optimizing dynamic communication and sensing task scheduling for low-altitude scenarios, characterized in that, It includes the following steps: S1: Build a communication and sensing integrated system including at least one drone, multiple ground users, and multiple ground sensing targets. Among them, the drone serves as an aerial access point to provide downlink communication services for ground users, and at the same time performs radar sensing on ground sensing targets; Divide the task window of the communication and sensing integrated system into several frames, each frame serving a different set of ground users; further divide each frame into several time slots, each time slot for separately executing a communication task, or separately executing a sensing task, or simultaneously executing a communication and a sensing task; S2: Build a communication model of the communication and sensing integrated system, calculate the communication spectral efficiency between the drone and each ground user according to the communication model, and estimate the Cramér-Rao bound of the departure angle between the drone and each ground sensing target; S3: Dynamically configure the sensing task scheduling factor of each time slot of the communication and sensing integrated system based on the adaptive ISAC algorithm to complete the dynamic scheduling of sensing tasks; S4: Based on the configured sensing task scheduling factor and the Cramér-Rao bound of the departure angle between the drone and each ground sensing target, introduce sensing constraints, and aim to maximize the communication spectral efficiency between the drone and each ground user, construct and solve the beamforming optimization problem corresponding to each frame to obtain the beam optimization result of each frame; S5: Based on the beam optimization result corresponding to each frame, construct a frame-multi-user matching problem; limit to match only a single user each time, adopt a deferred acceptance strategy with a preference update mechanism, iteratively solve the frame-multi-user matching problem to obtain the matching result between each frame and different sets of ground users, and complete the dynamic scheduling of communication tasks.

2. The dynamic communication and sensing task scheduling optimization method for low-altitude scenarios according to claim 1, wherein In step S1, the communication and sensing integrated system includes at least one drone, K single-antenna ground users, and J ground sensing targets; the drone is configured with a uniform linear array of M antennas and flies from a preset initial position to a target position within the task period; Use the drone as an aerial access point to provide downlink communication services for ground users and perform radar sensing on ground targets at the same time; Denote the index sets of ground users and ground perception targets as and Denote the planar position of the ground user as u k =(u k,x , u k,y ), and denote the planar position of the ground perception target as v j =(v j,x , v j,y ); Decompose the task cycle into multiple consecutive task windows, and optimize the entire task cycle by optimizing each task window; the duration of each task window is T, which contains frames, where T C is the frame length, and the frame index is denoted as Further divide each frame into time slots, where τ is the duration of each time slot, and the time slot index is denoted as Each task window contains a total of N = C·N C time slots, and (c, n) is used to represent the time slot indexes of different frames in each task window; Denote the planar position of the UAV as q[c,n]=(q x [c,n],q y [c,n]), where q x [c,n] and q y [c,n] are the X-axis and Y-axis coordinates of the UAV in time slot (c,n) respectively; assume that the UAV flies at a fixed height H that meets air traffic control regulations; The UAV transmits a signal matrix in time slot (c, n). where L is the length of the signal frame; the signal matrix X[c, n] satisfies X[c, n] = W[c, n]S[c, n], where contains data streams sent to K ground users. Assuming that the data streams are independent of each other, i.e., (·) H is the conjugate transpose operator; is the beamforming matrix, which is used to simultaneously achieve communication and sensing functions.

3. The dynamic communication and sensing task scheduling optimization method for low-altitude scenarios according to claim 2, characterized in that In the step S1, define the sensing task scheduling factor o[c,n] ∈ {0, 1} and the communication task scheduling factor z k [c,n] ∈ {0, 1} of the integrated communication and sensing system, which are respectively used to represent whether to execute the sensing task and whether to provide communication services for the ground user k in the time slot (c, n). Among them, o[c,n] = 1 means that the sensing task is executed in the time slot (c, n), and o[c,n] = 0 means that the sensing task is not executed in the time slot (c, n); z k [c,n] = 1 means that communication services are provided for the user k in the time slot (c, n), and z k [c,n] = 0 means that there is no communication with the user k in the time slot (c, n); At the same time, set the following constraint conditions for the communication and sensing integrated system: Each time slot must perform at least one communication or sensing task to ensure full utilization of the system's time resources, that is, it satisfies: Within the entire mission window T, any ground user communicates with the UAV at least once, that is, it satisfies: Given the upper limit \(Q\) of the number of ground users communicating in each time slot, and \(Q\geq K / C\), that is, it satisfies:

4. The dynamic communication-sensing task scheduling optimization method for low-altitude scenarios according to claim 3, wherein, In step S2, the communication model of the communication and sensing integrated system includes: a communication received signal sub-model and a sensing received signal sub-model; Construct the communication received signal sub-model based on the LOS channel model, assuming that the Doppler effect caused by the movement of the UAV has been fully compensated at the ground user end; the channel vector h of the UAV to the ground user k at the time slot (c, n) k is expressed as: where, β0 represents the channel power gain corresponding to the reference distance d0, represents the distance between the UAV and the ground user k, a k [c,n] represents the antenna steering vector pointing to the ground user k, satisfying d is the antenna spacing, λ is the wavelength, θ k [c,n] is the departure angle between the UAV and the ground user k, (·) T is the transpose operator; The signal y received by the ground user k at time slot (c, n) k [c, n] is expressed as: Among them, is the desired signal, represents the interference between users, represents the additive white Gaussian noise received by the ground user k, is the noise power; The communication spectral efficiency R of the ground user k in the time slot (c, n) k [c, n] is expressed as: The sensing received signal sub-model includes: Assume that the Doppler frequency shift caused by ground sensing targets and UAV movement has been fully compensated, and the ground sensing targets are modeled as unstructured point targets. Then, the sensing channel G j [c,n] of the ground sensing target j is expressed as: wherein, is the complex reflection coefficient, ∈ j represents the radar reflection surface of the ground sensing target j, e j [c,n] is the distance between the UAV and the ground sensing target j; in the case of a monostatic radar setting, θ j [c,n] is the departure angle between the UAV and the ground sensing target j; Estimate the departure angle θ between the UAV and each ground sensing target j The Cramer-Rao lower bound Πs(θ j [c,n]), is expressed as: Among them, represents A j [c,n]| θ the derivative of θ j with respect to [c,n], is the perceived noise level.

5. The dynamic communication and sensing task scheduling optimization method for low-altitude scenarios according to claim 4, wherein In the step S3, define the parameter change rate f at the i-th perception i S , which is used to dynamically adjust the perception task scheduling factor o[c,n], expressed as: Among them, is the preset key parameter at the i-th perception; f i S The value should be directly proportional to the parameter change rate in the adjacent perception period. Dynamically configuring the sensing task scheduling factor of each time slot of the communication and sensing integrated system based on the adaptive ISAC algorithm includes the following steps: S3.1: Initialize i = 1 and initialize parameters Perception interval Δ i , maximum perception interval Δ max and minimum perception interval Δ min ; S3.2: Calculate the parameter change rate f at the i-th perception i S , and compare f i S with the preset threshold . If , then increase the perception interval Δ i at the (i + 1)-th perception, denoted as Δ i = min{Δ i + 1, Δ max}; if , then decrease the perception interval Δ i at the (i + 1)-th perception, denoted as Δ i = max{Δ i - 1, Δ min}; where, min{·} represents taking the minimum value in the set, and max{·} represents taking the maximum value in the set; S3.3: Repeat step S3.2 until the sensing intervals of the entire task window are obtained, then terminate the iteration, and configure the sensing task scheduling factor o[c,n] for each time slot according to the obtained sensing intervals.

6. The dynamic communication and sensing task scheduling optimization method for low-altitude scenarios according to claim 5, characterized in that In step S4, the beamforming optimization problem is specifically: Among them, represents the set of ground users served within the c-th frame; η k represents the weight coefficient of ground user k, and the larger η k is, the higher the priority of ground user k in spectrum efficiency optimization; Constraint C1 is the sensing constraint, indicating that the value of the Cramér-Rao bound is less than the given threshold ξ to ensure that the sensing performance meets the preset requirements; Constraint C2 indicates that the transmission power of the UAV cannot exceed the maximum power P max ; Solve the beamforming optimization problem for each frame to obtain the optimal beam vector corresponding to each frame Use the optimal beam vector as the beam optimization result 7. The dynamic communication and sensing task scheduling optimization method for low-altitude scenarios according to claim 6, characterized in that In step S5, the frame-multi-user matching problem is specifically: wherein, is the optimal solution obtained by solving problem P1 under a given Γ c , and the corresponding objective function value, |Γ | represents the number of ground users served within the c-th frame. c ​ 8. The dynamic communication and sensing task scheduling optimization method for low-altitude scenarios according to claim 7, wherein In step S5, the frame-multi-user matching problem is iteratively solved according to the following steps: S5.1: Initialization: Initialize all ground users and time slots as unmatched, and set the reservation list of each frame Calculate the preference list F between each frame and ground users based on the following formula c,k : S5.2: Update the reservation list: Each ground user, according to the preference list F obtained by initialization c,k sends a matching request to the most preferred frame c * ; After each frame receives the matching requests from ground users, it screens the requesting users according to its preference list. When multiple requests are received simultaneously, only the most preferred B ground users are selected to enter the reservation list, and other ground users are rejected, where B is a preset positive integer; S5.3: One-to-one matching: The rejected ground user k sends a matching request to the next optimal frame that can be matched according to the current preference list; after each frame receives a new ground user request, the following matching decision is made in combination with the current reservation list: First, select the most preferred ground user k from the union of the request set of the new ground user and the current reservation list * Complete the final matching; then update the B less preferred users to the current reservation list, and reject the requests of all other ground users after the update; S5.4: Matching and preference update: After the matching is completed, the ground user k * is added to the set Γ c , and is excluded from subsequent matching; if a certain frame reaches its capacity limit, the frame is marked as unavailable and excluded from subsequent matching; S5.5: The rejected ground users dynamically adjust their preference lists according to the latest matching results and repeat steps S5.3 - S5.4 until all ground users complete the matching, and output the final set Γ c .

9. The dynamic communication and sensing task scheduling optimization method for low-altitude scenarios according to claim 8, wherein, Construct the following problem: Among them, among them and represent the set of rejected users and the set of frames that do not reach the capacity limit, respectively; is a binary variable. When the terrestrial user k is the most preferred request for the c-th frame in the i-th iteration, otherwise Constraint C9 restricts that each terrestrial user can be matched with at most one frame; Constraint C10 restricts that each frame can select at most one terrestrial user for matching during a single iteration; Solve problem P3. In step S5.2, update the reserved list according to the solution result of problem P3; in step S5.3, update the request set of the new ground users according to the solution result of problem P3.

10. The dynamic communication and sensing task scheduling optimization method for low-altitude scenarios according to claim 9, characterized in that Solve problem P1 based on the continuous convex approximation algorithm; solve problem P3 based on the Hungarian algorithm.

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