ISAC network optimization method and system for multi-unmanned aerial vehicle cooperative positioning communication

Abstract

The invention discloses an ISAC network optimization method and system for multi-unmanned-aerial-vehicle cooperative positioning communication, and relates to the technical field of sixth-generation mobile communication, and the method comprises the steps: obtaining observation data of multiple unmanned aerial vehicles based on a multi-unmanned-aerial-vehicle cooperative positioning and communication framework, integrating the observation data through a data fusion mechanism, and obtaining fusion state data; establishing a joint optimization problem by taking maximization of the average reachable rate of the system as a target function and combining constraint conditions; solving a joint optimization problem by adopting a hybrid actor-commentator successive convex optimization algorithm, decomposing the joint optimization problem into three sub-problems of associated variable optimization, beam forming optimization and unmanned aerial vehicle flight path optimization, and alternately solving the three sub-problems until a target function is converged, and obtaining an optimal correlation variable, an optimal beam forming vector and an optimal unmanned aerial vehicle flight path. According to the invention, through cooperative work of multiple UAVs, the positioning precision and communication performance of the mobile vehicle are improved, and system energy consumption and service quality are balanced at the same time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of the sixth generation mobile communication technology, and in particular to an ISAC network optimization method and system for multi-UAV cooperative positioning communication. BACKGROUND

[0002] With the accelerated evolution of the sixth generation mobile communication technology, vehicle-to-everything (V2X) as a core application scenario of 6G puts forward higher requirements for the comprehensive capability of the network. Under this background, the integrated sensing and communication (ISAC) technology, with the core advantages of sharing spectrum, hardware and signal resources, can break through the limitations of the separation of traditional sensing and communication functions, realize the cooperative optimization of the two, and become a key feature supporting 6G network.

[0003] Currently, the landing application of the ISAC technology mainly depends on two types of technical solutions: one is the traditional ISAC system based on ground fixed platform, which forms a network by deploying base stations, sensing devices and other devices on the ground to provide services for surrounding vehicles; the other is the ISAC solution assisted by a single unmanned aerial vehicle (UAV), which takes advantage of the aerial deployment of the UAV to break through the geographical limitations of the ground platform and expand the service coverage.

[0004] Research has found that the existing technical solutions have obvious defects in actual application and are difficult to meet the high performance requirements of the V2X scenario. Among them, the traditional ground fixed platform ISAC system is significantly affected by geographical environmental factors such as terrain obstruction and building obstruction: in complex areas such as urban dense building groups and mountainous areas, the sensing signal is easily blocked, resulting in a significant decrease in sensing accuracy, and the communication link may be interrupted due to obstruction or multipath effect, which cannot guarantee the continuity and reliability of the service. While the ISAC solution assisted by a single UAV can avoid the geographical limitations of the ground platform, it has problems such as insufficient positioning accuracy and limited coverage, making it difficult to meet the centimeter-level positioning requirements of scenarios such as autonomous driving.

[0005] In addition, existing research still has obvious deficiencies in improving the sensing accuracy of multiple UAVs, adapting sensing-communication resources in dynamic scenarios, and balancing energy and performance. SUMMARY

[0006] In view of the deficiencies of the existing technology, the present application provides an ISAC network optimization method and system for multi-UAV cooperative positioning communication, which improves the positioning accuracy and communication performance of mobile vehicles through the cooperative work of multiple UAVs, while balancing the energy consumption and service quality of the system.

[0007] In order to achieve the above purpose, the present application adopts the following technical solution: In a first aspect, the present application provides an ISAC network optimization method for multi-UAV cooperative positioning communication, comprising the following steps: A multi-UAV cooperative positioning and communication framework is constructed, observation data of the multi-UAV is obtained based on the multi-UAV cooperative positioning and communication framework, the observation data is integrated through a data fusion mechanism to obtain fusion state data; Based on the fusion state data, a joint optimization problem including association variables, beamforming vectors and UAV flight trajectories is established with a maximum system average achievable rate as an objective function and in combination with constraint conditions; The joint optimization problem is solved by using a hybrid actor-critic successive convex optimization algorithm, the joint optimization problem is decomposed into three sub-problems of association variable optimization, beamforming optimization and UAV flight trajectory optimization, the three sub-problems are solved alternately until the objective function converges, and optimal association variables, optimal beamforming vectors and optimal UAV flight trajectories are obtained.

[0008] As a further technical solution, the data fusion mechanism is that a state vector matrix of a moving vehicle perceived by a UAV is defined, the state vector matrix includes positioning state and speed state of the moving vehicle, observation data is converted into dimensionless values of a uniform scale by using a normalized distance metric, abnormal data in the observation data is removed based on a maximum likelihood criterion to obtain multi-source effective observation data, and the multi-source effective observation data is integrated by mean fusion to obtain fusion state data of the moving vehicle.

[0009] As a further technical solution, a calculation formula of the system average achievable rate is: ; wherein, is a bandwidth, is a time slot of a moving vehicle and a UAV .

[0010] As a further technical solution, the constraint conditions include a minimum perception performance constraint on the objective function, a UAV transmission power constraint, a UAV total power constraint, a UAV speed constraint, a UAV minimum safety distance constraint and an association variable binary constraint.

[0011] As a further technical solution, a solving process of the association variable optimization sub-problem is that a binary association variable is first relaxed into a continuous variable, and a Charnes-Cooper transformation is used to convert the objective function into a linear form, then a CVX tool is used to solve a linear programming problem, and finally a threshold is used to determine a value of the binary association variable.

[0012] As a further technical solution, the solving process of the beamforming optimization sub-problem is: first, define the beamforming matrix, and convert the rank constraint of the beamforming matrix into a semi-positive definite constraint by using the semi-definite relaxation method; then, by using the successive convex optimization method, the non-convex problem is converted into a convex problem by approximating the objective function and the constraint condition by using the first-order Taylor expansion.

[0013] As a further technical solution, the solving process of the UAV flight trajectory optimization sub-problem is: first, based on the soft actor-critic algorithm, a Markov decision process is constructed, and the state space, action space and reward function are defined; wherein the state space includes the UAV position, channel information state and residual energy, and the reward function is a composite reward function including the average reachable rate, speed penalty, safety distance penalty and beam gain penalty; then, the actor-critic network is trained to optimize the UAV flight trajectory.

[0014] In a second aspect, the present application provides an ISAC network optimization system for multi-UAV cooperative positioning and communication, comprising the following modules: A data fusion module is configured to: construct a multi-UAV cooperative positioning and communication framework, obtain observation data of the multi-UAV based on the multi-UAV cooperative positioning and communication framework, and integrate the observation data by a data fusion mechanism to obtain fused state data; An optimization problem construction module is configured to: based on the fused state data, taking maximizing the system average reachable rate as the objective function, and combining the constraint conditions, establish a joint optimization problem including the association variable, the beamforming vector and the UAV flight trajectory; An algorithm solving module is configured to: solve the joint optimization problem by using a hybrid actor-critic successive convex optimization algorithm, decompose the joint optimization problem into three sub-problems of association variable optimization, beamforming optimization and UAV flight trajectory optimization, and alternately solve the three sub-problems until the objective function converges, and obtain the optimal association variable, the optimal beamforming vector and the optimal UAV flight trajectory.

[0015] The one or more technical solutions of the present application have the following beneficial effects: The present application constructs a multi-UAV cooperative positioning and communication framework, integrates the observation data of the multi-UAV by a data fusion mechanism to obtain fused state data, and the multi-UAV obtains vehicle observation information from different spatial perspectives, which can effectively offset the environmental noise interference and measurement error when a single UAV observes, significantly improves the accuracy of mobile vehicle position and speed estimation, can meet the stringent requirements of centimeter-level positioning in the automatic driving scene, and solves the core defects that the traditional single-UAV solution is difficult to adapt to high-precision positioning scenarios.

[0016] This invention uses multiple unmanned aerial vehicles (UAVs) as aerial sensing and communication nodes. Leveraging the flexibility of UAV deployment, it easily overcomes the limitations of complex geographical environments such as densely built-up urban areas and mountainous regions, avoiding problems like signal blockage and communication link interruptions. Simultaneously, the spatially distributed network formed by multiple UAVs achieves wider coverage, reduces service blind spots, and ensures the continuity and reliability of sensing and communication services for mobile vehicles during dynamic operation, compensating for the poor geographical adaptability of ground-based fixed platforms.

[0017] This invention incorporates related variables, beamforming vectors, and UAV flight trajectories into the optimization framework when establishing a joint optimization problem, with the objective function being to maximize the system's average reachability. By alternately solving the three sub-problems using a hybrid actor-commentator successive convex optimization algorithm, the service relationship between the UAV and the vehicle, beamforming parameters, and flight trajectory can be dynamically adjusted based on vehicle movement status and channel environment changes. This achieves precise adaptation of sensing and communication resources, significantly improving the system's average reachability, meeting the communication needs of high-speed mobile vehicles in V2X scenarios, and solving the problems of low resource utilization and unstable communication performance in dynamic scenarios.

[0018] This invention fully incorporates constraints when establishing a joint optimization problem, maximizing the system's average reachability while strictly controlling the UAV's energy consumption. By optimizing the UAV's flight trajectory to reduce ineffective energy consumption and by optimizing related variables and beamforming vectors to avoid power waste, it achieves a synergistic balance between system performance and energy consumption. This effectively extends the UAV's operating time, ensures the continuous service capability of multi-UAV networks, and solves the problem of performance being difficult to balance under energy constraints in existing technologies. Attached Figure Description

[0019] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0020] Figure 1 This is a framework diagram of the multi-UAV collaborative positioning MV of the present invention; Figure 2 This is a framework diagram of the UAV trajectory optimization algorithm based on SAC of this invention; Figure 3 This is a flowchart of the iterative optimization process of the HASCO algorithm of this invention. Detailed Implementation

[0021] Example 1 This embodiment provides an ISAC network optimization method for multi-UAV cooperative positioning and communication, such as... Figure 3 As shown, the specific steps of the method are as follows: S1: Construct a multi-UAV cooperative positioning and communication framework, acquire observation data from multiple UAVs based on the multi-UAV cooperative positioning and communication framework, and integrate the observation data through a data fusion mechanism to obtain fused state data.

[0022] In step S1, as Figure 1 As shown, consider a network in which a drone (UAV) locates a moving vehicle (MV) and communicates with the vehicle. rack equipped and A millimeter-wave massive MIMO radar communication dual-function (DRFC) UAV, composed of uniform planar array (UPA) transmit and receive antennas, forms a space-distributed observation network, providing ground-based... A single antenna MV provides communication services.

[0023] In this embodiment, the flight time of the UAV is... Discretized There are 1 time slot, and the length of each time slot is 1. Using sets respectively , and This represents the set of drones, vehicles, and time slots. It considers the mobility of both UAVs and MVs, assuming the UAVs are at a fixed altitude. Flight, MV's horizontal altitude is To provide high-quality wireless coverage, the UAV needs to acquire the specific location information of the MV to form a directional beam. Therefore, this embodiment employs... and These refer to unmanned aerial vehicles (UAVs). And moving vehicles (MV) In the time slot The horizontal position coordinates.

[0024] In step S1, the multi-UAV cooperative positioning and communication framework has the function of "positioning-communication-sensing", which specifically includes: channel model, signal model, radar measurement model, and data fusion model.

[0025] Channel model: Unmanned Aerial Vehicles (UAVs) To moving vehicle (MV) Channel State Information (CSI) is represented as follows: ; in, The wavelength representing the air-to-ground carrier frequency. Indicates in time slot UAV To MV distance, This is the launch guidance vector.

[0026] Signal model: The ISAC signal transmitted by a UAV, after beamforming, is represented as follows: ; in, For binary associative variables, For beamforming vectors, For information signals.

[0027] The signals received by the moving vehicle (MV) are: ; in, For MV Additive White Gaussian Noise (AWGN) at the receiver.

[0028] In this embodiment, in time slot Moving Vehicle (MV) The signal-to-noise ratio (SINR) at a given point is expressed as: ; Unmanned Aerial Vehicles (UAVs) The expression for the received echo signal is: ; in, Represents the complex radar cross section (RCS). This is due to the Doppler frequency shift caused by movement. For round-trip time delay, For UAV Additive white Gaussian noise (AWGN) at the receiver. To receive the guide vector.

[0029] Radar measurement model: Matched filtering is used to process the echo signal, and time delay and Doppler shift information are obtained from the peak values. Multiple Signal Classification (MUSIC) algorithm is used to estimate the vertical and horizontal departure angles (AoD). Therefore, the position and velocity of MV are estimated based on time delay, Doppler shift, and vertical and horizontal AoD as follows:

[0030] in, , , , ,and Represents the speed of light. Indicates the carrier frequency. UAV and MV radial velocity, , , , For measuring noise.

[0031] Data fusion model: The data fusion mechanism is as follows: Define a state vector matrix of the mobile vehicle perceived by the UAV, which includes the positioning and speed states of the mobile vehicle; use a normalized distance metric to convert the observation data into dimensionless values ​​of a uniform scale; remove outlier data from the observation data based on the maximum likelihood criterion to obtain multi-source effective observation data; and integrate the multi-source effective observation data through mean fusion to obtain the fused state data of the mobile vehicle.

[0032] In this embodiment, using Indicates in time slot UAV Perceived MV The state vector matrix, where, , These represent the positioning status and velocity status, respectively. First, a normalized distance metric is used to convert measurements of different dimensions and units into dimensionless values ​​of a unified scale, facilitating subsequent data matching and fusion.

[0033] In this embodiment, the location information is defined as Euclidean distance (ED). Euclidean distance of motion state .in, UAV Perceived MV The positioning state vector, UAV Perceived MV The motion state vector.

[0034] Therefore, the normalized ED is expressed as: ; in, and Let represent the maximum values ​​of the Euclidean distance for location information and the Euclidean distance for motion state, respectively. According to the maximum likelihood (ML) criterion, if... and and and The spatial distance should be close to zero, that is... The smaller the value, the better for the drone. and UAV The same goal is perceived; conversely, A larger value indicates a greater difference in target information. To quantify whether different UAVs are sensing the same target, a threshold is defined. .when At that time, it was believed that UAV and The same MV is being perceived; when If the data is deemed to be from a different target or is abnormal, it must be removed.

[0035] Through hard data fusion, MV The fusion location and velocity fusion estimates are as follows: ; ; Among them, set , For set The number of UAVs in the system.

[0036] S2: Based on fused state data, with the objective function of maximizing the average reachability of the system, and combined with constraints, a joint optimization problem is established that includes correlated variables, beamforming vectors, and UAV flight trajectories.

[0037] In step S2, the joint optimization problem is established with maximizing the average reachability of the system as the objective function:

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] The formula for calculating the system's average reachability is as follows: ;in, For bandwidth, For time slots China Mobile Vehicles With drones The signal-to-noise ratio between them.

[0045] Constraint C1 represents the minimum perception performance constraint on the objective function; C2 and C3 represent the UAV transmit power constraint and the UAV total power constraint, respectively; C4 and C5 represent the UAV speed constraint and the UAV minimum safe distance constraint, respectively; and C6 represents the binary constraint of the associated variable.

[0046] S3: The Hybrid Actor-Critic Successive Convex Optimization (HASCO) algorithm is used to solve the joint optimization problem. The joint optimization problem is decomposed into three sub-problems: correlation variable optimization, beamforming optimization, and UAV flight trajectory optimization. The three sub-problems are solved alternately until the objective function converges, and the optimal correlation variable, optimal beamforming vector, and optimal UAV flight trajectory are obtained.

[0047] In step S3, the specific steps of the Hybrid Actor-Critic Successive Convex Optimization (HASCO) algorithm include: Solving the subproblem of optimizing related variables: First, relax the binary correlation variables. The variables are continuous, and the Charnes-Cooper transformation is used to convert the objective function into a linear form; Then, the CVX tool is used to solve the linear programming problem. Finally, the value of the binary correlation variable is determined based on the threshold (0.5).

[0048] Solving the beamforming optimization subproblem: First, the beamforming matrix is ​​defined as follows: Furthermore, a semidefinite relaxation method is used to transform the rank constraint of the beamforming matrix into a positive semidefinite constraint. Then, by using the successive convex optimization method, the non-convex problem is transformed into a convex problem for solution through the first-order Taylor expansion to approximate the objective function and constraints.

[0049] Solving the subproblem of optimizing the flight trajectory of unmanned aerial vehicles (UAVs): First, a Markov decision process is constructed based on the soft actor-critic (SAC) algorithm, and the state space, action space, and reward function are defined. The state space includes the UAV's position, channel information state, and remaining energy. The reward function is a composite reward function that includes average reachability, speed penalty, safe distance penalty and beam gain penalty; Then as Figure 2 As shown, the drone's flight trajectory is optimized by training an actor-critic network.

[0050] In step S3, the optimization of the above three sub-problems is performed alternately until the difference between the objective function values ​​of two adjacent iterations is less than the convergence threshold. .

[0051] Based on the obtained optimal correlation variables, optimal beamforming vector, and optimal UAV flight trajectory, the UAV scheduling strategy, beamforming vector, and flight trajectory are adjusted to optimize system performance.

[0052] This embodiment effectively improves the positioning accuracy and communication performance of mobile vehicles through multi-UAV collaborative work and intelligent optimization algorithms, providing a feasible solution for integrated sensing and communication applications in 6G vehicle-to-everything (V2X) networks.

[0053] Example 2 This embodiment provides an ISAC network optimization system for multi-UAV cooperative positioning and communication, including the following modules: The data fusion module is configured to: construct a multi-UAV cooperative positioning and communication framework, acquire observation data from multiple UAVs based on the multi-UAV cooperative positioning and communication framework, and integrate the observation data through a data fusion mechanism to obtain fused state data; The optimization problem construction module is configured to: based on fused state data, with the objective function of maximizing the average reachability of the system, and in combination with constraints, establish a joint optimization problem including correlated variables, beamforming vectors, and UAV flight trajectories; The algorithm solution module is configured to: use a hybrid actor-critic successive convex optimization algorithm to solve the joint optimization problem, decompose the joint optimization problem into three sub-problems: correlation variable optimization, beamforming optimization, and UAV flight trajectory optimization, and solve the three sub-problems alternately until the objective function converges, and obtain the optimal correlation variable, the optimal beamforming vector, and the optimal UAV flight trajectory.

[0054] Various modifications and variations of this invention will be apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An ISAC network optimization method for multi-UAV cooperative positioning and communication, characterized in that, The method comprises the steps of: constructing a multi-unmanned aerial vehicle cooperative positioning and communication framework, obtaining observation data of the multi-unmanned aerial vehicles based on the multi-unmanned aerial vehicle cooperative positioning and communication framework, and integrating the observation data through a data fusion mechanism to obtain fused state data; based on the fused state data, taking the maximization of system average achievable rate as an objective function, combining constraint conditions, and establishing a joint optimization problem containing association variables, beamforming vectors and unmanned aerial vehicle flight trajectories; the joint optimization problem is solved by using a hybrid actor-critic successive convex optimization algorithm, the joint optimization problem is decomposed into three sub-problems of association variable optimization, beamforming optimization and unmanned aerial vehicle flight trajectory optimization, the three sub-problems are solved alternately until the objective function converges, and the optimal association variable, the optimal beamforming vector and the optimal unmanned aerial vehicle flight trajectory are obtained.

2. The ISAC network optimization method for multi-UAV cooperative positioning communication according to claim 1, wherein, The data fusion mechanism is that: a mobile vehicle state vector matrix perceived by the unmanned aerial vehicle is defined, the state vector matrix contains the positioning state and the speed state of the mobile vehicle; the observation data is converted into dimensionless values of a uniform scale by using a normalized distance measure; based on the maximum likelihood criterion, abnormal data in the observation data is removed to obtain multi-source effective observation data, and the multi-source effective observation data is integrated by mean fusion to obtain the fused state data of the mobile vehicle.

3. The ISAC network optimization method for multi-UAV cooperative positioning communication of claim 1, wherein, The calculation formula of the system average achievable rate is: ; wherein, is a bandwidth, is a time slot mobile vehicles with drones signal-to-noise ratio between.

4. The ISAC network optimization method for multi-UAV cooperative positioning communication of claim 1, wherein, The constraint conditions include: minimum perception performance constraint of the objective function, unmanned aerial vehicle transmission power constraint, unmanned aerial vehicle total power constraint, unmanned aerial vehicle speed constraint, unmanned aerial vehicle minimum safety distance constraint and association variable binary constraint.

5. The ISAC network optimization method for multi-UAV cooperative positioning communication of claim 1, wherein, The solving process of the association variable optimization sub-problem is that: firstly, the binary association variable is relaxed into a continuous variable, and the Charnes-Cooper transformation is used to convert the objective function into a linear form; then the linear programming problem is solved by using the CVX tool; finally, the value of the binary association variable is determined according to the threshold.

6. The ISAC network optimization method for multi-UAV cooperative positioning communication of claim 1, wherein, The solving process of the beamforming optimization sub-problem is that: firstly, the beamforming matrix is defined, and the semi-definite relaxation method is used to convert the rank constraint of the beamforming matrix into a semi-positive definite constraint; then, by using the successive convex optimization method, the objective function and the constraint condition are approximated by the first-order Taylor expansion, and the non-convex problem is converted into a convex problem for solving.

7. The ISAC network optimization method for multi-UAV cooperative positioning communication of claim 1, wherein, The solving process of the unmanned aerial vehicle flight trajectory optimization sub-problem is that: firstly, a Markov decision process is constructed based on a soft actor-critic algorithm, and a state space, an action space and a reward function are defined; wherein, the state space includes the unmanned aerial vehicle position, the channel information state and the remaining energy, and the reward function is a composite reward function containing the average achievable rate, the speed penalty, the safety distance penalty and the beam gain penalty; then, the actor-critic network is trained to optimize the unmanned aerial vehicle flight trajectory.

8. The ISAC network optimization system for multi-UAV cooperative positioning and communication, characterized in that, The method comprises the following modules: a data fusion module configured to construct a multi-unmanned aerial vehicle cooperative positioning and communication framework, obtain observation data of the multi-unmanned aerial vehicles based on the multi-unmanned aerial vehicle cooperative positioning and communication framework, and integrate the observation data through a data fusion mechanism to obtain fused state data; The optimization problem construction module is configured to: based on the fusion state data, taking the system average reachable rate as an objective function, combined with constraint conditions, establish a joint optimization problem containing the association variable, the beamforming vector and the UAV flight trajectory; The algorithm solving module is configured to: adopt a mixed actor-critic successive convex optimization algorithm to solve the joint optimization problem, decompose the joint optimization problem into three sub-problems of association variable optimization, beamforming optimization and UAV flight trajectory optimization, and alternately solve the three sub-problems until the objective function converges, and obtain the optimal association variable, the optimal beamforming vector and the optimal UAV flight trajectory.

9. A computer-readable storage medium having stored thereon a program, characterized in that, The program is executed by the processor to implement the steps in the ISAC network optimization method for multi-UAV cooperative positioning communication of claim 1-7.

10. An electronic device comprising a memory, a processor, and a program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps in the ISAC network optimization method for multi-UAV cooperative positioning communication of claim 1-7.

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