Low-altitude unmanned aerial vehicle integrated sensing and communication beamforming method based on queue stability

Abstract

The application discloses a low-altitude unmanned aerial vehicle communication and perception integrated beamforming method based on queue stability, which comprises the following steps: establishing a system model under a low-altitude unmanned aerial vehicle communication and perception integrated scene; the system model comprises a communication and perception signal propagation model between an unmanned aerial vehicle and a ground user and a data transmission queue model at the unmanned aerial vehicle end; establishing a long time slot optimization problem of the low-altitude unmanned aerial vehicle communication and perception integrated beamforming, converting the long time slot optimization problem into a short time slot optimization problem based on a current time slot state; solving the short time slot optimization problem to obtain a beamforming weight vector meeting the communication performance, the perception performance and the queue stability requirements. The application can effectively balance the transmission power consumption and the data transmission stability of the unmanned aerial vehicle system while guaranteeing the communication and perception performances, and realizes minimization of long-term average power consumption.

Description

Technical Field

[0001] This invention relates to a low-altitude unmanned aerial vehicle (UAV) integrated sensing beamforming method based on queue stability, belonging to the field of UAV communication technology. Background Technology

[0002] In recent years, the scale of business in low-altitude scenarios, with UAVs as the core carrier, has expanded rapidly, creating an urgent need for improved resource utilization efficiency and transmission reliability in low-altitude UAV communication. Integrated Sensing and Communication Architecture (ISAC) significantly improves the resource utilization efficiency of low-altitude UAV systems by sharing spectrum and hardware resources and collaboratively achieving communication data transmission and environmental perception functions within the same signal frame. Simultaneously, beamforming technology, through precise control of the antenna array's radiation pattern, can focus signal energy towards the target user, effectively improving communication link quality and sensing accuracy, and is a key means to ensure the performance of integrated sensing and communication architectures for low-altitude UAVs.

[0003] However, due to the limited payload and endurance of low-altitude drones, reducing system power consumption while ensuring service performance has become a key consideration for the practical application of drone-based integrated sensing technology. Furthermore, in real-world applications, user data arrivals are random and unpredictable. If the communication link capacity cannot dynamically match the fluctuations in data arrivals, data accumulation can easily occur at the drone end, leading to increased transmission latency or even queue overflow, severely impacting service quality.

[0004] Therefore, in dynamic and stochastic environments, how to collaboratively optimize beamforming strategies to minimize the long-term average power consumption of the system while ensuring communication and sensing performance and maintaining the stability of the data transmission queue has become a critical technical problem that urgently needs to be solved. Existing solutions mostly focus on static or instantaneous performance optimization, failing to effectively consider the long-term dynamics of the queue state and the continuous constraints of power consumption, and lacking a systematic low-power, high-stability beamforming design method. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a low-altitude UAV integrated sensing beamforming method based on queue stability. This method can effectively balance the transmission power consumption and data transmission stability of the UAV system while ensuring communication and sensing performance, thereby minimizing long-term average power consumption. To achieve the above objective, this invention employs the following technical solution:

[0006] In a first aspect, the present invention provides a low-altitude unmanned aerial vehicle (UAV) integrated sensing beamforming method based on queue stability, comprising:

[0007] Establish a system model for a low-altitude unmanned aerial vehicle (UAV) integrated sensing scenario; wherein, the system model includes a communication and sensing signal propagation model between the UAV and ground users, and a data transmission queue model at the UAV end;

[0008] With the goal of minimizing the average transmit power of the system model over long time slots, and constrained by the stability limit of the data transmission queue, the lower limit of the information reachability rate of the communication user, the lower limit of the beam gain of the sensing user, and the upper limit of the transmit power of the UAV, a long time slot optimization problem of integrated sensing and communication beamforming for low-altitude UAVs is established, which is then transformed into a short time slot optimization problem based on the current time slot state.

[0009] Solving the short time slot optimization problem yields beamforming weight vectors that satisfy communication performance, sensing performance, and queue stability requirements.

[0010] In conjunction with the first aspect, optionally, the low-altitude UAV sensing integrated scenario includes a system equipped with... A drone with a uniform planar array, a ground communication user with a single antenna, and a ground sensing user with a single antenna;

[0011] The communication and sensing signal propagation model between the UAV and ground users includes:

[0012] exist At any given moment, the drone transmits signals to the user. It can be expressed by the following formula:

[0013] ,

[0014] in, for Data signal at time, for Real-time perception signals, for The beamforming weight vector of the UAV's communication transmission at any given moment. for The perceived beamforming weight vector at any given moment;

[0015] Air-to-ground channel vectors from UAVs to communication and sensing users It can be expressed by the following formula:

[0016] ,

[0017] in, For communication users or sensing users, For communication users, To understand users, Rice's fading factor For the line-of-sight component of the channel vector, For the non-line-of-sight components of the channel vector;

[0018] exist At any given time, the information achievable rate of communication users It can be expressed by the following formula:

[0019] ,

[0020] in, for The conjugate transpose of the air-to-ground channel vector from the UAV to the communication user at any given time. The noise power of the communication user;

[0021] exist At all times, sense the user's beam gain It can be expressed by the following formula:

[0022] ,

[0023] in, for The conjugate transpose of the air-to-ground channel vector from the drone to the sensing user.

[0024] In conjunction with the first aspect, optionally, the data transmission queue model at the UAV end includes:

[0025] initialization The queue backlog at time is Data arrival volume is The information achievable rate for communication users is ;

[0026] The queue update method is expressed by the following formula:

[0027] ,

[0028] in, for Queue backlog at any given time. To obtain the maximum value, the amount of data received. It follows a Poisson distribution;

[0029] The data transmission queue model on the UAV side satisfies the queue stability constraint, and the long-slot average value of the queue backlog is bounded, expressed by the following formula:

[0030] ,

[0031] in, This represents the minimum upper bound of the average expected value of the queue. The total duration of the long time slot, To find the mean.

[0032] In conjunction with the first aspect, optionally, the long-slot optimization problem of low-altitude UAV integrated sensing beamforming is established with the objective of minimizing the average transmit power of the system model over long time slots, and constrained by the stability limitations of the data transmission queue, the lower limit of the information achievable rate for communication users, the lower limit of the beam gain for sensing users, and the upper limit of the UAV transmit power. This problem is expressed by the following formula:

[0033] ,

[0034] ,

[0035] ,

[0036] ,

[0037] ,

[0038] in, for The beamforming weight vector of the UAV's communication transmission at any given moment. for The perceived beamforming weight vector at any given time. This refers to the total duration of the long time slot; This represents the minimum upper bound of the average expected value of the queue. To find the mean, for Queue backlog at any given time; For the information achievable rate of communication users, This is the information transmission rate threshold for communication users; To sense the user's beam gain, To measure the distance from drones to sensing users, To sense the user's beam gain constraint threshold; This is the maximum transmission power.

[0039] In conjunction with the first aspect, optionally, the transformation of the long time slot optimization problem into a short time slot optimization problem based on the current time slot state is expressed by the following formula:

[0040] ,

[0041] ,

[0042] ,

[0043] ,

[0044] in, As a weighting factor, To omit the time index The beamforming weight vector for communication launched by the UAV. To omit the time index The perceived beamforming weight vector, To omit the time index queue backlog To omit the time index The information achievable rate for communication users; This is the information transmission rate threshold for communication users; To omit the time index The perceived beam gain of the user To measure the distance from drones to sensing users, To sense the user's beam gain constraint threshold; This is the maximum transmission power.

[0045] In conjunction with the first aspect, optionally, the solution of the short time slot optimization problem to obtain a beamforming weight vector that satisfies the requirements of communication performance, sensing performance, and queue stability includes:

[0046] By introducing auxiliary variables, the beamforming weight vector is transformed into a covariance matrix, and the short time slot optimization problem is equivalently reconstructed into a semidefinite programming problem with the covariance matrix as the variable. The semidefinite programming problem includes non-convex rank-one constraints.

[0047] The non-convex communication rate constraint in the semidefinite programming problem is approximated as a convex constraint by performing a first-order Taylor expansion. The penalty function method is adopted, and the rank-one constraint is added as a penalty term to the objective function, thus transforming the semidefinite programming problem into a convex optimization problem.

[0048] A convex optimization solver is used to iteratively solve the convex optimization problem until convergence, thus obtaining the beamforming weight vector.

[0049] Secondly, the present invention provides a low-altitude unmanned aerial vehicle (UAV) integrated sensing beamforming device based on queue stability, comprising:

[0050] The first module is used to establish a system model for a low-altitude UAV with integrated sensing and communication. The system model includes a communication and sensing signal propagation model between the UAV and ground users, and a data transmission queue model for the UAV.

[0051] The second module is used to establish a long time-slot optimization problem for low-altitude UAV integrated sensing beamforming, with the goal of minimizing the average transmit power of the system model over long time slots, and with constraints such as the stability limit of the data transmission queue, the lower limit of the information reachability rate of the communication user, the lower limit of the beam gain of the sensing user, and the upper limit of the UAV transmit power. The long time-slot optimization problem is transformed into a short time-slot optimization problem based on the current time-slot state.

[0052] Solver module: Used to solve short time slot optimization problems and obtain beamforming weight vectors that meet the requirements of communication performance, sensing performance and queue stability.

[0053] Thirdly, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the low-altitude UAV integrated sensing beamforming method based on queue stability described in the first aspect.

[0054] Fourthly, the present invention provides a computer device, comprising:

[0055] Memory, used to store computer programs / instructions;

[0056] A processor for executing the computer program / instructions to implement the steps of the low-altitude UAV integrated sensor beamforming method based on queue stability as described in the first aspect.

[0057] Fifthly, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the low-altitude UAV integrated sensor beamforming method based on queue stability described in the first aspect.

[0058] Compared with the prior art, the beneficial effects achieved by the low-altitude UAV integrated sensing beamforming method based on queue stability provided in this embodiment of the invention include:

[0059] This invention establishes a system model for low-altitude unmanned aerial vehicles (UAVs) in a sensor-integrated scenario. The system model includes a communication and sensing signal propagation model between the UAV and ground users, and a data transmission queue model for the UAV. With the objective of minimizing the average long-slot transmit power of the system model, and constrained by the stability limitations of the data transmission queue, the lower limit of the information reachability rate for communication users, the lower limit of the beam gain for sensing users, and the upper limit of the UAV transmit power, a long-slot optimization problem for sensor-integrated beamforming of low-altitude UAVs is established. This invention introduces the stability constraints of the data transmission queue into the sensor-integrated beamforming design. By establishing a long-slot optimization problem for sensor-integrated beamforming of low-altitude UAVs, communication performance, sensing performance, and queue stability are considered uniformly at the system level. This effectively solves the technical problem of traditional methods having single performance indicators and difficulty in balancing power consumption and data transmission reliability, enabling UAVs to significantly reduce long-term operating power consumption and extend endurance while ensuring service quality.

[0060] This invention transforms the long time-slot optimization problem into a short time-slot optimization problem based on the current time-slot state; it decomposes the complex long time-slot optimization problem into a series of short time-slot optimization problems that depend only on the current channel state and queue state and can be solved online in real time; this not only makes mathematically difficult long-term problems solvable, but also lays the theoretical foundation for the online implementation and engineering application of the algorithm.

[0061] This invention solves the short time-slot optimization problem to obtain beamforming weight vectors that meet the requirements of communication performance, sensing performance, and queue stability. The invention introduces auxiliary variables to transform the beamforming weight vectors into a covariance matrix, equivalently reconstructing the short time-slot optimization problem into a semidefinite programming problem with the covariance matrix as the variable. This semidefinite programming problem includes non-convex rank-one constraints. A first-order Taylor expansion is performed on the non-convex communication rate constraints in the semidefinite programming problem, approximating them as convex constraints. A penalty function method is used, incorporating the rank-one constraints as a penalty term into the objective function, transforming the semidefinite programming problem into a convex optimization problem. A convex optimization solver iteratively solves the convex optimization problem until convergence, yielding the beamforming weight vectors. This invention can quickly calculate beamforming weight vectors that meet the requirements of communication performance, sensing performance, and queue stability in each time slot, ensuring the optimality of the solution and the convergence of the algorithm. This results in a beamforming scheme with good engineering feasibility, dynamically adapting to channel changes and data traffic fluctuations, and continuously meeting multi-dimensional performance requirements. Attached Figure Description

[0062] Figure 1 This is a flowchart illustrating the integrated sensory beamforming method for low-altitude unmanned aerial vehicles based on queue stability in Embodiment 1 of the present invention.

[0063] Figure 2This is a schematic diagram of the low-altitude UAV sensory integration scenario in the low-altitude UAV sensory integration beamforming method based on queue stability in Embodiment 1 of the present invention;

[0064] Figure 3 This is a simulation diagram of the communication beam gain map generated by the low-altitude UAV integrated sensing beamforming method based on queue stability in Embodiment 2 of the present invention.

[0065] Figure 4 This is a simulation diagram of the sensing beam gain map generated by the low-altitude UAV sensing-sensing integrated beamforming method based on queue stability in Embodiment 2 of the present invention.

[0066] Figure 5 This is a schematic diagram of the simulation results of the system average power consumption and average queue length as a function of the trade-off factor V in the low-altitude UAV integrated sensor beamforming method based on queue stability in Embodiment 2 of the present invention.

[0067] Figure 6 This is a simulation comparison diagram of the system power consumption over time under different data arrival rates in the low-altitude UAV integrated sensing beamforming method based on queue stability in Embodiment 2 of the present invention, with / without queue stability considerations. Detailed Implementation

[0068] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0069] Example 1:

[0070] like Figure 1 As shown, this embodiment provides a low-altitude UAV integrated sensing beamforming method based on queue stability, including:

[0071] Establish a system model for a low-altitude unmanned aerial vehicle (UAV) integrated sensing scenario; wherein, the system model includes a communication and sensing signal propagation model between the UAV and ground users, and a data transmission queue model at the UAV end;

[0072] With the goal of minimizing the average transmit power of the system model over long time slots, and constrained by the stability limit of the data transmission queue, the lower limit of the information reachability rate of the communication user, the lower limit of the beam gain of the sensing user, and the upper limit of the transmit power of the UAV, a long time slot optimization problem of integrated sensing and communication beamforming for low-altitude UAVs is established, which is then transformed into a short time slot optimization problem based on the current time slot state.

[0073] Solving the short time slot optimization problem yields beamforming weight vectors that satisfy communication performance, sensing performance, and queue stability requirements.

[0074] The specific steps are as follows.

[0075] Step 1: Establish a system model for the integrated sensing scenario of low-altitude UAVs.

[0076] like Figure 2 As shown, the low-altitude UAV integrated sensing scenario includes a UAV acting as an airborne base station, a ground communication user with a single antenna, and a ground sensing user with a single antenna.

[0077] The drone is equipped with A uniform planar array composed of antenna elements.

[0078] Assume the drone hovers above the origin of a three-dimensional Cartesian coordinate system at a height of [missing information]. The location, the horizontal coordinate of the communication user is The user's horizontal coordinate is .

[0079] The system model includes a communication and sensing signal propagation model between the UAV and ground users, and a data transmission queue model on the UAV side.

[0080] Step 1.1: Establish a communication and sensing signal propagation model between the UAV and ground users.

[0081] exist At any given moment, the drone transmits signals to the user. It can be expressed by the following formula:

[0082] ,(1)

[0083] In equation (1), for Data signal at time, for Real-time perception signals, for The beamforming weight vector of the UAV's communication transmission at any given moment. for The perceived beamforming weight vector at any given time.

[0084] Specifically, Data signal at time satisfy , Moment-time perception signal satisfy ,and and ;in, To find the mean.

[0085] Specifically, Beamforming weight vector for UAV communication at any given moment satisfy , The perceived beamforming weight vector at time t satisfies The communication beamforming weight vector and the sensing beamforming weight vector of the UAV are the core variables that need to be optimized.

[0086] Air-to-ground channel vectors from UAVs to communication and sensing users It can be expressed by the following formula:

[0087] (2),

[0088] In equation (2), For communication users or sensing users, For communication users, To understand users, Rice's fading factor For the line-of-sight component of the channel vector, This represents the non-line-of-sight component of the channel vector.

[0089] The air-to-ground channel vectors from the UAV to the communication user G and the sensing user S are calculated using equation (2).

[0090] Channel vector line-of-sight components It can be expressed by the following formula:

[0091] (3)

[0092] In equation (3), , At the speed of light, For carrier frequency.

[0093] In equation (3), The distance from the drone to the user, This represents the loss coefficient for the line-of-sight path.

[0094] In equation (3), The pitch angle of the drone from the user is expressed as... , The azimuth angle of the drone from the user is expressed as , Here is the user's horizontal coordinate; when the user is a communication user, the communication user's horizontal coordinate is... When the user is a sensing user, the horizontal coordinate of the sensing user is: .

[0095] In equation (3), Antenna array direction vector It can be expressed by the following formula:

[0096] (4)

[0097] In equation (4), is a natural constant and the base of the natural logarithm; The imaginary unit, and These are the antenna elements along the uniform planar array. shaft and Index number in the axial direction. and They are uniform planar arrays in shaft and The total number of antenna elements along the axial direction satisfies the following conditions: and .

[0098] Channel vector non-line-of-sight components It can be expressed by the following formula:

[0099] (5)

[0100] In equation (5), It is the number of non-line-of-sight paths. This represents the loss coefficient for non-line-of-sight paths. For the first The drone's pitch angle along the path to the user. For the first The azimuth angle of the drone along the specified path to the user. It is the first The complex coefficients of the path.

[0101] exist At any given time, the information achievable rate of communication users It can be expressed by the following formula:

[0102] (6)

[0103] In equation (6), for The conjugate transpose of the air-to-ground channel vector from the UAV to the communication user at any given time. This refers to the noise power of the communication user.

[0104] Using the transmit beam gain toward the sensing user as a metric for sensing performance, At all times, sense the user's beam gain It can be expressed by the following formula:

[0105] (7)

[0106] In equation (7), for The conjugate transpose of the air-to-ground channel vector from the drone to the sensing user.

[0107] Step 1.2: Establish a data transmission queue model for the UAV.

[0108] Unmanned aerial vehicles (UAVs) are equipped with data storage queues as aerial communication base stations to mitigate the instability of downlink communication data capacity caused by changes in wireless channels.

[0109] initialization The queue backlog at time is Data arrival volume is The information achievable rate for communication users is .

[0110] The queue update method is expressed by the following formula:

[0111] (8)

[0112] In equation (8), for Queue backlog at any given time. To obtain the maximum value, the amount of data received. It follows a Poisson distribution, i.e. .

[0113] To ensure queue stability, the data transmission queue model on the UAV side satisfies queue stability constraints, and the long-slot average value of queue backlog is bounded, expressed by the following formula:

[0114] (9)

[0115] In equation (9), This represents the minimum upper bound of the average expected value of the queue. The total duration of the long time slot, To find the mean.

[0116] Step 2: With the goal of minimizing the average transmit power of the system model over long time slots, and with constraints such as the stability limit of the data transmission queue, the lower limit of the information reachability rate of the communication user, the lower limit of the beam gain of the sensing user, and the upper limit of the transmit power of the UAV, a long time slot optimization problem of integrated sensing and communication beamforming for low-altitude UAVs is established. The long time slot optimization problem is then transformed into a short time slot optimization problem based on the current time slot state.

[0117] Step 2.1: With the objective of minimizing the long-slot average transmit power of the system model, and constrained by the stability limitations of the data transmission queue, the lower limit of the information reachability rate for communication users, the lower limit of the beam gain for sensing users, and the upper limit of the UAV transmit power, a long-slot optimization problem P1 for integrated sensing and communication beamforming of low-altitude UAVs is established, expressed by the following equation:

[0118] (10a)

[0119] (10b)

[0120] (10c)

[0121] (10d)

[0122] ,(10e)

[0123] In equation (10a), for The beamforming weight vector of the UAV's communication transmission at any given moment. for The perceived beamforming weight vector at any given time. This refers to the total duration of the long time slot;

[0124] Equation (10b) guarantees the stability requirements of the data queue, where, This represents the minimum upper bound of the average expected value of the queue. To find the mean, for Queue backlog at any given time;

[0125] In equation (10c), For the information achievable rate of communication users, This is the information transmission rate threshold for communication users;

[0126] In equation (10d), To sense the user's beam gain, To measure the distance from drones to sensing users, To sense the user's beam gain constraint threshold;

[0127] In equation (10e), This is the maximum transmission power.

[0128] Step 2.2: Transform the long time slot optimization problem into a short time slot optimization problem based on the current time slot state.

[0129] For the queue stability constraint (10b), the slot association method based on Lyapunov transforms the long slot optimization problem into a short slot optimization problem based on the current slot state.

[0130] First, to measure the backlog in the queue, we define the following quadratic Lyapunov function:

[0131] (11)

[0132] Obviously, It is always non-negative and exhibits a quadratic growth; the larger the value, the more severe the queue backlog.

[0133] Next, to examine the change of this function between two consecutive time slots and measure the tendency of queue instability, Lyapunov drift is introduced and defined as:

[0134] (12)

[0135] Clearly, the expected change based on the Lyapunov function can measure the queue backlog in different time slots. For a given time slot... Current queue state The stability of the queue is ensured by minimizing Lyapunov drift.

[0136] To minimize system energy consumption while ensuring queue stability, in addition to considering the expected changes in queue size, a penalty term needs to be introduced to balance system energy consumption. Therefore, the following objective function is defined, which includes Lyapunov drift and a penalty term:

[0137] (13)

[0138] In equation (13), It is a non-negative weighting parameter that represents the importance of the system's total transmit power relative to queue stability, i.e., the trade-off factor.

[0139] Theorem 1: Assuming that the arrival of user data and the channel are bounded random variables, then the following inequality holds:

[0140] (14)

[0141] In equation (14), It is a positive finite constant.

[0142] Proof: According to formula (8), It has the following upper bound:

[0143] (15)

[0144] Equation (15) uses the following inequality:

[0145] (16)

[0146] Based on the power constraint (10e), the communication signal beamforming matrix and sensing signal beamforming matrix Since it is bounded, the communication rates of users are also bounded. Let... and for and The finite maximum value. Substituting formula (15) into formula (12), we get:

[0147] (17)

[0148] In equation (17), , To find the mean.

[0149] In addition, due to Formula (17) can be used to further determine the lower bound:

[0150] (18)

[0151] In equation (18), .

[0152] According to Theorem 1, the upper bound of the Lyapunov drift-penalty function is:

[0153] , (19).

[0154] because It is a constant, which allows the optimization objective to shift to minimizing the second and third terms without affecting optimality. However, the expectation in the objective function remains difficult to handle because the data is unknown. Therefore, the chance expectation minimization technique is used to minimize the upper bound of the objective function.

[0155] Therefore, in any The start of the moment, based on the known data storage queue The value of transforms the long-slot optimization problem P1 into a short-slot optimization problem P2, which is expressed by the following formula:

[0156] , (20a)

[0157] (20b)

[0158] (20c)

[0159] (20d)

[0160] In equation (20a), As a weighting factor, To omit the time index The beamforming weight vector for communication launched by the UAV. To omit the time index The perceived beamforming weight vector, To omit the time index queue backlog To omit the time index The information achievable by the communication user.

[0161] In equation (20b), This is the information transmission rate threshold for communication users.

[0162] In formula (20c), To omit the time index The perceived beam gain of the user To measure the distance from the drone to the user. To sense the user's beam gain constraint threshold.

[0163] In formula (20d), This is the maximum transmission power.

[0164] Step 3: Solve the short time slot optimization problem to obtain the beamforming weight vector that meets the requirements of communication performance, sensing performance and queue stability.

[0165] Step 3.1: Introduce auxiliary variables to convert the beamforming weight vector into a covariance matrix, and reconstruct the short time slot optimization problem P2 into a semidefinite programming problem P3 with the covariance matrix as the variable. The semidefinite programming problem includes non-convex rank-one constraints.

[0166] Introduce auxiliary variables { }, and define the beamforming covariance matrix as and , must meet , and rank-one constraint .

[0167] The short-slot optimization problem P2 is equivalently reconstructed into a semidefinite programming problem P3, expressed by the following formula:

[0168] (21a)

[0169] (21b)

[0170] ,(21c)

[0171] (21d)

[0172] , (21e)

[0173] , (21f)

[0174] (21g)

[0175] in, In order to find traces, This is the conjugate transpose of the air-to-ground channel vector from the UAV to the communication user. This refers to the air-to-ground channel vector from the UAV to the communication user. This is the conjugate transpose of the air-to-ground channel vector from the UAV to the sensing user. This is the air-to-ground channel vector from the UAV to the sensing user.

[0176] Step 3.2: Perform a first-order Taylor expansion on the non-convex communication rate constraint in the semidefinite programming problem P3 to approximate it as a convex constraint. Use the penalty function method to add the rank-one constraint as a penalty term to the objective function, thus transforming the semidefinite programming problem P3 into a convex optimization problem P4.

[0177] For the non-convex constraint (21b) regarding the rate, through mathematical transformation, it can be transformed into the following form:

[0178] ,(twenty two),

[0179] At feasible points to By performing a first-order Taylor expansion approximation, we obtain the linear constraints:

[0180] ,(twenty three).

[0181] The penalty function method is used to solve the non-convex constraint (21g).

[0182] Specifically, due to Equivalent to ,in and They are and The largest eigenvalue. Then the rank-one constraint is replaced by a penalty function with a tradeoff factor. Furthermore, and Since it is a non-convex function, perform a first-order Taylor expansion on it:

[0183] ,(24a)

[0184] (24b)

[0185] in, and It is a feasible solution. and They are matrices and The eigenvector corresponding to the largest eigenvalue.

[0186] Based on the above approximation results, the semidefinite programming problem P3 is transformed into a convex optimization problem P4, which is expressed by the following formula:

[0187] (25a)

[0188] , (25b).

[0189] Step 3.3: Use a convex optimization solver to iteratively solve the convex optimization problem P4 until convergence, and obtain the beamforming weight vector.

[0190] As a preferred option, the optimization problem P4 is solved using CVX convex optimization numerical calculation software based on MATLAB simulation software to obtain the UAV transmission communication beamforming weight vector and sensing beamforming weight vector.

[0191] This embodiment introduces the stability constraint of the data transmission queue into the integrated sensing beamforming design. By establishing a long time slot optimization problem for integrated sensing beamforming of low-altitude UAVs, it considers communication performance, sensing performance and queue stability in a unified manner at the system level. It effectively solves the technical problem of single performance indicators and difficulty in balancing power consumption and data transmission reliability in traditional methods. This enables UAVs to significantly reduce long-term operating power consumption and extend flight time while ensuring service quality.

[0192] This embodiment decomposes the complex long time-slot optimization problem into a series of short time-slot optimization problems that depend only on the current channel state and queue state and can be solved online in real time. This not only makes the mathematically difficult long-term problem solvable, but also lays the theoretical foundation for the online implementation and engineering application of the algorithm.

[0193] Example 2:

[0194] Based on the low-altitude UAV integrated sensor beamforming method based on queue stability provided in Embodiment 1, this embodiment implements the algorithm and verifies its performance.

[0195] In each time slot The UAV executes the following loop: 1) Obtain the current channel estimate , and queue state 2) Using the current queue Using the reference point (initially set to a zero matrix or a random matrix) as input, run the convex optimization solver to solve the convex optimization problem P4, obtaining the UAV transmission communication beamforming weight vector and the sensing beamforming weight vector. 3) Use the weight vectors to transmit signals and adjust the signal according to the service data rate. Update queue status to .

[0196] To verify the effectiveness of the method of the present invention, simulation experiments were conducted.

[0197] The initial system parameter settings are as follows:

[0198] UAV array size hovering height The horizontal coordinate of the communication user is The user's horizontal coordinate is Information transmission rate threshold for communication users Beam gain constraint threshold for sensing users Maximum transmit power Trade-off factors .

[0199] Figure 3 A simulation diagram of the communication beam gain map generated using the method of Embodiment 1 is shown. The communication beam is mainly focused in the direction of the communication user to ensure the reachability requirements of the communication user. At the same time, the communication beam also has a high gain in the direction of the sensing user to assist the sensing beam in meeting the sensing requirements.

[0200] Figure 4 A simulation diagram of the sensing beam gain map generated using the method of Example 1 is shown. The sensing beam is mainly focused in the direction of the sensing user, while a neutral line is generated in the direction of the communication user to avoid interference in the direction of the communication user.

[0201] The second setting for the system parameters is as follows:

[0202] UAV array size hovering height The horizontal coordinate of the communication user is The user's horizontal coordinate is Information transmission rate threshold for communication users Beam gain constraint threshold for sensing users Maximum transmit power Data arrival rate .

[0203] Figure 5 This diagram illustrates the simulation results of the system's average power consumption and average queue length as a function of the tradeoff factor V, using the method described in Example 1. The system's average power consumption varies with the tradeoff factor V. The average queue length decreases with the increase of the weighting factor, while the average queue length decreases with the increase of the weighting factor. The increase is due to the increase in the trade-off factor. It is a key parameter used to adjust the balance between system power consumption and queue length, when the trade-off factor When the size is small, the system focuses more on reducing queue backlog, but the system power consumption is higher; while when the trade-off factor is small... When the value is large, the system tends to reduce system power consumption, resulting in larger queue backlogs and latency. Therefore, the trade-off factor... The value must be within a reasonable range to achieve a reasonable balance between system power consumption and stable queue length.

[0204] The third setting for system parameters is as follows:

[0205] UAV array size hovering height The horizontal coordinate of the communication user is The user's horizontal coordinate is Information transmission rate threshold for communication users Beam gain constraint threshold for sensing users Maximum transmit power Trade-off factors .

[0206] Figure 6 The diagram illustrates the stability considerations with and without queues using the method described in Example 1, along with a simulation comparison of system power consumption over time under different data arrival rates. As can be seen from the diagram, after 1200 time slots, in the queued scheme, system power consumption tends to stabilize, and system power consumption increases with the average data arrival rate. However, in the queueless scheme, because data arriving at each time slot needs to be sent out at every moment, system power consumption fluctuates significantly.

[0207] In summary, the low-altitude UAV integrated sensing beamforming method based on queue stability provided in Example 1 can quickly calculate the beamforming weight vector that meets the requirements of communication performance, sensing performance and queue stability in each time slot, ensuring the optimality of the solution and the convergence of the algorithm. This makes the proposed beamforming scheme have good engineering feasibility and can dynamically adapt to channel changes and data traffic fluctuations, continuously meeting multi-dimensional performance requirements.

[0208] Example 3:

[0209] This embodiment provides a low-altitude UAV integrated sensing beamforming device based on queue stability, comprising:

[0210] The first module is used to establish a system model for a low-altitude UAV with integrated sensing and communication. The system model includes a communication and sensing signal propagation model between the UAV and ground users, and a data transmission queue model for the UAV.

[0211] The second module is used to establish a long time-slot optimization problem for low-altitude UAV integrated sensing beamforming, with the goal of minimizing the average transmit power of the system model over long time slots, and with constraints such as the stability limit of the data transmission queue, the lower limit of the information reachability rate of communication users, the lower limit of the beam gain of sensing users, and the upper limit of the transmit power of UAVs. The long time-slot optimization problem is transformed into a short time-slot optimization problem based on the current time-slot state.

[0212] Solver module: Used to solve short time slot optimization problems and obtain beamforming weight vectors that meet the requirements of communication performance, sensing performance and queue stability.

[0213] Example 4:

[0214] This embodiment provides a computer-readable storage medium storing a computer program / instruction thereon. When the computer program / instruction is executed by a processor, it implements the steps of the low-altitude UAV integrated sensing beamforming method based on queue stability described in Embodiment 1.

[0215] Example 5:

[0216] This embodiment provides a computer device, including:

[0217] Memory, used to store computer programs / instructions;

[0218] A processor is used to execute the computer program / instructions to implement the steps of the low-altitude UAV integrated sensor beamforming method based on queue stability described in Embodiment 1.

[0219] Example 6:

[0220] This embodiment provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the low-altitude UAV integrated sensor beamforming method based on queue stability described in Embodiment 1.

[0221] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0222] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0223] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0224] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0225] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A sensor-integrated beamforming method for low-altitude unmanned aerial vehicles (UAVs) based on queue stability, characterized in that, include: Establish a system model for a low-altitude unmanned aerial vehicle (UAV) integrated sensing scenario; wherein, the system model includes a communication and sensing signal propagation model between the UAV and ground users, and a data transmission queue model at the UAV end; The low-altitude UAV integrated sensing scenario includes a system equipped with... A drone with a uniform planar array, a ground communication user with a single antenna, and a ground sensing user with a single antenna; The communication and sensing signal propagation model between the UAV and ground users includes: exist At any given moment, the drone transmits signals to the user. It can be expressed by the following formula: ， in, for Data signals at any given time, for Real-time sensing signals, for The beamforming weight vector of the UAV's communication transmission at any given moment. for The perceived beamforming weight vector at any given moment; Air-to-ground channel vectors from UAVs to communication and sensing users It can be expressed by the following formula: ， in, For communication users or sensing users, For communication users, To understand users, Rice's fading factor For the line-of-sight component of the channel vector, For the non-line-of-sight components of the channel vector; exist At any given time, the information achievable rate of communication users It can be expressed by the following formula: ， in, for The conjugate transpose of the air-to-ground channel vector from the UAV to the communication user at any given time. The noise power of the communication user; exist At all times, sense the user's beam gain It can be expressed by the following formula: ， in, for The conjugate transpose of the air-to-ground channel vector from the drone to the sensing user; The data transmission queue model at the UAV end includes: initialization The queue backlog at time is Data arrival volume is The information achievable rate for communication users is ; The queue update method is expressed by the following formula: ， in, for Queue backlog at any given time. To obtain the maximum value, the amount of data received. It follows a Poisson distribution; The data transmission queue model on the UAV side satisfies the queue stability constraint, and the long-slot average value of the queue backlog is bounded, expressed by the following formula: ， in, This represents the minimum upper bound of the average expected value of the queue. The total duration of the long time slot, To find the mean; With the goal of minimizing the average transmit power of the system model over long time slots, and constrained by the stability limit of the data transmission queue, the lower limit of the information reachability rate of the communication user, the lower limit of the beam gain of the sensing user, and the upper limit of the transmit power of the UAV, a long time slot optimization problem of integrated sensing and communication beamforming for low-altitude UAVs is established, which is then transformed into a short time slot optimization problem based on the current time slot state. Solving the short time slot optimization problem yields beamforming weight vectors that satisfy communication performance, sensing performance, and queue stability requirements.

2. The low-altitude UAV integrated sensing beamforming method based on queue stability according to claim 1, characterized in that, The above describes a long-slot optimization problem for integrated sensing and communication beamforming of low-altitude UAVs, with the objective of minimizing the average transmit power over long time slots. This problem is constrained by limitations on data transmission queue stability, lower bounds on the information reachability rate of communication users, lower bounds on the beam gain of sensing users, and upper bounds on the transmit power of the UAV. The problem is expressed by the following equation: ， ， ， ， ， in, for The beamforming weight vector of the UAV's communication transmission at any given moment. for The perceived beamforming weight vector at any given time. This refers to the total duration of the long time slot; This represents the minimum upper bound of the average expected value of the queue. To find the mean, for Queue backlog at any given time; For the information achievable rate of communication users, This is the information transmission rate threshold for communication users; To sense the user's beam gain, To measure the distance from drones to sensing users, To sense the user's beam gain constraint threshold; This is the maximum transmission power.

3. The low-altitude UAV integrated sensing beamforming method based on queue stability according to claim 1, characterized in that, The transformation of the long-slot optimization problem into a short-slot optimization problem based on the current slot state is expressed by the following formula: ， ， ， ， in, As a weighting factor, To omit the time index The beamforming weight vector for communication launched by the UAV. To omit the time index The perceived beamforming weight vector, To omit the time index queue backlog To omit the time index The information achievable rate for communication users; This is the information transmission rate threshold for communication users; To omit the time index The perceived beam gain of the user To measure the distance from the drone to the user. To sense the user's beam gain constraint threshold; This is the maximum transmission power.

4. The low-altitude UAV integrated sensing beamforming method based on queue stability according to claim 1, characterized in that, Solving the short time slot optimization problem yields beamforming weight vectors that satisfy communication performance, sensing performance, and queue stability requirements, including: By introducing auxiliary variables, the beamforming weight vector is transformed into a covariance matrix, and the short time slot optimization problem is equivalently reconstructed into a semidefinite programming problem with the covariance matrix as the variable. The semidefinite programming problem includes non-convex rank-one constraints. The non-convex communication rate constraint in the semidefinite programming problem is approximated as a convex constraint by performing a first-order Taylor expansion. The penalty function method is adopted, and the rank-one constraint is added as a penalty term to the objective function, thus transforming the semidefinite programming problem into a convex optimization problem. A convex optimization solver is used to iteratively solve the convex optimization problem until convergence, thus obtaining the beamforming weight vector.

5. A low-altitude unmanned aerial vehicle (UAV) integrated sensor beamforming device based on queue stability, characterized in that, include: The first module is used to establish a system model for a low-altitude UAV with integrated sensing and communication. The system model includes a communication and sensing signal propagation model between the UAV and ground users, and a data transmission queue model for the UAV. The low-altitude UAV integrated sensing scenario includes a system equipped with... A drone with a uniform planar array, a ground communication user with a single antenna, and a ground sensing user with a single antenna; The communication and sensing signal propagation model between the UAV and ground users includes: exist At any given moment, the drone transmits signals to the user. It can be expressed by the following formula: ， in, for Data signals at any given time, for Real-time sensing signals, for The beamforming weight vector of the UAV's communication transmission at any given moment. for The perceived beamforming weight vector at any given moment; Air-to-ground channel vectors from UAVs to communication and sensing users It can be expressed by the following formula: ， in, For communication users or sensing users, For communication users, To understand users, Rice's fading factor, For the line-of-sight component of the channel vector, For the non-line-of-sight components of the channel vector; exist At any given time, the information achievable rate of communication users It can be expressed by the following formula: ， in, for The conjugate transpose of the air-to-ground channel vector from the UAV to the communication user at any given time. The noise power of the communication user; exist At all times, sense the user's beam gain It can be expressed by the following formula: ， in, for The conjugate transpose of the air-to-ground channel vector from the drone to the sensing user; The data transmission queue model at the UAV end includes: initialization The queue backlog at time is Data arrival volume is The information achievable rate for communication users is ; The queue update method is expressed by the following formula: ， in, for Queue backlog at any given time. To obtain the maximum value, the amount of data received. It follows a Poisson distribution; The data transmission queue model on the UAV side satisfies the queue stability constraint, and the long-slot average value of the queue backlog is bounded, expressed by the following formula: ， in, This represents the minimum upper bound of the average expected value of the queue. The total duration of the long time slot, To find the mean; The second module is used to establish a long time-slot optimization problem for low-altitude UAV integrated sensing beamforming, with the goal of minimizing the average transmit power of the system model over long time slots, and with constraints such as the stability limit of the data transmission queue, the lower limit of the information reachability rate of the communication user, the lower limit of the beam gain of the sensing user, and the upper limit of the UAV transmit power. The long time-slot optimization problem is transformed into a short time-slot optimization problem based on the current time-slot state. Solver module: Used to solve short time slot optimization problems and obtain beamforming weight vectors that meet the requirements of communication performance, sensing performance and queue stability.

6. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the low-altitude UAV integrated sensing beamforming method based on queue stability as described in any of claims 1-4.

7. A computer device, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the low-altitude UAV integrated sensor beamforming method based on queue stability as described in any one of claims 1-4.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the low-altitude UAV integrated sensor beamforming method based on queue stability as described in any one of claims 1-4.

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