A method and system for minimizing energy consumption of multi-antenna UAV communication

Abstract

The present invention relates to the field of UAV communication technology, and specifically discloses a method and system for minimizing the energy consumption of multi-antenna UAV communications. First, an aerial video surveillance system based on a multi-antenna rotorcraft UAV is constructed, allowing a multi-antenna UAV (UAV) to provide services to multiple GUs (ground users) at the same time. Then, by jointly optimizing the UAV's flight trajectory, flight time, and transmit beamforming, an optimization problem is constructed with minimizing the UAV's total energy consumption as the optimization goal, while meeting the user's QoS requirements. The optimization problem is further solved. In order to solve the optimization problem, a path discretization method is first used in combination with a golden section search method to determine the UAV's flight time and flight trajectory, thereby minimizing the UAV's propulsion energy consumption, and then minimizing the UAV's communication energy consumption. Simulation results show that the method and system are significantly superior to existing benchmark solutions in terms of energy consumption, demonstrating high efficiency and practicality.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method and system for minimizing energy consumption in multi-antenna UAV communication. Background Art

[0002] Unmanned aerial vehicles (UAVs), due to their high maneuverability and flexible deployment capabilities, are often used as aerial base stations to establish high-quality wireless connections with ground users (GUs). Compared to traditional terrestrial networks, UAVs can provide superior communication link quality and deliver on-demand, timely services based on user requests. UAVs have a wide range of applications, including natural gas pipeline inspections, emergency rescue operations, and fire monitoring. These scenarios require UAVs and GUs to form an efficient video transmission network. Furthermore, in urban security inspections, UAVs must be able to precisely fly and hover in confined spaces to provide real-time video surveillance services for emergency situations such as fires, traffic accidents, or public safety incidents. Compared to fixed-wing UAVs, rotary-wing UAVs are more popular due to their superior maneuverability and flexibility. They can perform precise operations and stable flight in confined spaces, making them particularly suitable for tasks requiring rapid response and high-quality video capture.

[0003] In recent years, the field of UAV video transmission has made significant progress and introduced a variety of innovative methods. For example, a study proposed a UAV anti-interference video transmission scheme based on reinforcement learning (RL), which can ensure the quality of experience (QoE) of the video without relying on the video service model, while effectively reducing energy consumption. Another study proposed a QoE-driven, UAV-supported pseudo-analog wireless video transmission scheme, which aims to maximize the minimum peak signal-to-noise ratio of the GU video reconstruction quality by jointly optimizing the transmission power allocation strategy and UAV trajectory. In addition, a study proposed a UAV-assisted communication scheme to improve the communication service quality (QoS) of edge users. These innovative methods provide new ideas and technical support for the application of UAV video streaming.

[0004] However, existing research has primarily focused on single-antenna UAVs, a configuration that allows them to communicate with only one GU at a time, limiting their ability to communicate with multiple GUs simultaneously. This limitation reduces system throughput and increases latency in video transmission.

[0005] On the other hand, the capacity of UAV onboard batteries is limited by their size and payload capacity, restricting the sustainability of UAV flight. Rotary-wing UAVs, in particular, typically have short battery life. During video transmission, battery consumption increases rapidly, limiting flight time and range, adversely impacting long-term surveillance missions. Therefore, reducing the overall energy consumption of UAVs while ensuring the quality of video streaming service is a key challenge.

[0006] Several studies have explored various energy consumption issues for UAVs. However, these studies have not considered video streaming applications with QoS requirements. QoS effectively reflects the unique characteristics of video streaming and is an important metric for measuring video transmission performance. Minimizing the total energy consumption of UAVs while meeting user QoS requirements remains a challenging problem. Summary of the Invention

[0007] The present invention provides a method and system for minimizing the energy consumption of multi-antenna UAV communications, which solves the technical problems of how to apply multi-antenna UAVs for high-performance wireless communications and how to minimize the total energy consumption of multi-antenna UAVs while meeting user service quality requirements.

[0008] To solve the above technical problems, the present invention provides a method for minimizing energy consumption of multi-antenna UAV communication, comprising the following steps:

[0009] Build an aerial video surveillance system based on a multi-antenna rotary-wing UAV. The aerial video surveillance system consists of K single-antenna ground users and a rotary-wing UAV equipped with M ≥ 2 uniform rectangular array antennas. The UAV takes off from a specified starting point and flies to a specified destination at a fixed altitude. During the flight, it captures video and provides video transmission services to users.

[0010] With the goal of minimizing the total energy consumption of the UAV, under the constraint of ensuring that each ground user can achieve the minimum acceptable playback rate, under the constraints of the UAV's starting and ending positions, and under the UAV's flight speed not exceeding its maximum speed V max Under the constraint of , we construct an optimization problem of jointly optimizing the UAV’s transmit beamforming, trajectory, and flight time T;

[0011] Solve the optimization problem to obtain the trajectory, transmit beamforming and flight time T of the UAV.

[0012] Furthermore, the total energy consumption of the UAV is equal to the propulsion power P of the UAV at time t fly (t) and the transmission power P com The integral of the sum of (t) within the flight time T; the propulsion power P of the UAV at time t fly(t) is equal to the sum of the basic power consumption consumed by the UAV to overcome the air resistance at time t, the driving power consumption of the rotor, and the induced power consumption of the rotor; the UAV's transmission power P at time t com (t) is equal to the sum of the squares of the moduli of the beamforming vectors assigned to the K terrestrial users.

[0013] Furthermore, the basic power consumption of the drone to overcome the air resistance at time t is equal to The driving power consumption of the UAV rotor at time t is equal to The induced power consumption of the UAV rotor at time t is equal to v0, Ω, and r represent the induced mean speed of the rotor, the angular velocity of the rotor blades, and the radius of the rotor, respectively. P0 and P i They represent the blade profile power and induced power in the hovering state, d0, s, A r , ρ represent the fuselage drag ratio, rotor robustness, rotor disc area and air density respectively, v(t) represents the velocity vector of the UAV at time t, and || || represents the modular operation of the vector.

[0014] Furthermore, the minimum acceptable playback rate that each terrestrial user can achieve is expressed as the channel bandwidth B and the achievable video playback rate R of terrestrial user k at time t. k The product of (t) is not less than the minimum acceptable playback rate R min , R k (t) is equal to log2(1+SINR k (t)), SINR k (t) represents the signal-to-noise ratio of terrestrial user k at time t; calculate SINR k (t), a Racian channel model including line-of-sight and non-line-of-sight multipath components is adopted.

[0015] Furthermore, solving the optimization problem specifically includes the following steps:

[0016] Given the UAV’s transmit beamforming {w k (t)}, transforming the optimization problem into the first sub-problem of jointly optimizing the trajectory {q(t)} and flight time T of the UAV;

[0017] The path discretization method is used to divide the flight trajectory of the UAV into L segments, thereby obtaining L+1 path points; each path is regarded as a straight line, and it is assumed that the velocity vector v of the UAV in each path l is l Considered as constant, given the coordinates q of the drone at the beginning and end of the path segment l l and q l+1, transforming the first sub-problem into the second sub-problem, which aims to minimize the propulsion energy consumption of the UAV along the lth segment path, with ||q l+1 -q l ||≤d max , 0≤V l ≤V max Optimize the duration δ of each path for the constraints l , d max Indicates the maximum length of any path allowed, V l =||v l || is the flight speed of the UAV along the lth path;

[0018] Solve the second sub-problem and get the duration δ l , and further obtain the flight time T and the optimal flight trajectory {q(t)} of the UAV;

[0019] Substitute the obtained flight time T and the optimal flight trajectory {q(t)} of the UAV and transform the optimization problem into minimizing As the target, B log2(1+SINR k(l) )≥R min Optimizing the transmit beamforming of UAV for constraints {w k (t)}, the third sub-problem, SINR k(l) represents the signal-to-noise ratio of ground user k on line segment l;

[0020] Solve the third sub-problem and obtain the optimal UAV transmit beamforming {w k (t)}.

[0021] Furthermore, in the process of solving the second sub-problem, the golden section search method is used to obtain the optimal speed of each path segment, which specifically includes the following steps:

[0022] 1) Let x represent the speed V l , the function f(x) represents the flight loss of the UAV on the lth path Determine the initial interval [a,b] of x to be [0,V max ];

[0023] 2) Using the golden ratio, calculate x1 = a + 0.382(ba) and x2 = a + b – x1;

[0024] 3) Calculate the function values ​​f1 = f(x1) and f2 = f(x2) at points x1 and x2;

[0025] 4) Determine whether f1 is greater than f2. If so, set a = x1, x2 = a + b - x1, and return to step 3); if not, set b = x2, x1 = a + b - x2, and return to step 3);

[0026] 5) When the interval [a, b] is less than the specified precision ∈ 1, end the iteration of steps 3) and 4) and output the current interval [a, b];

[0027] 6) Determine the optimal speed that minimizes the flight energy consumption of the drone in each path and select it arbitrarily in the interval [a, b].

[0028] Furthermore, the optimal flight trajectory of the drone is to fly in a straight line at a constant speed between a specified starting point and a specified end point.

[0029] Furthermore, the third sub-problem is solved to obtain the optimal UAV transmit beamforming {w k (t)}, specifically comprising the steps of:

[0030] The third subproblem is transformed into minimizing As the target, B log2(1+SINR k )≥R min Optimize {w k}The fourth subproblem, w k 、SINR k Represents w in a single time period k (t), SINR k(l) ;

[0031] The constraint B log2(1+SINR k )≥R min Transformed into h i is the channel gain vector corresponding to user i, and γ is defined as equal to Then add |h i w i | 2 Replace with its lower bound Get new constraints Re{} represents the real part operation, Indicates w i Any feasible point of , thereby transforming the fourth sub-problem into the fifth sub-problem;

[0032] Solve the fifth sub-problem and get the optimal solution

[0033] Furthermore, a convex optimization problem solving tool is used to solve the fifth sub-problem.

[0034] The present invention also provides a multi-antenna UAV communication energy consumption minimization system, the key of which is: it includes an intelligent agent, and the intelligent agent is used to execute the multi-antenna UAV communication energy consumption minimization method.

[0035] The present invention provides a method and system for minimizing the energy consumption of multi-antenna UAV communication. First, an aerial video surveillance system based on a multi-antenna rotorcraft UAV is constructed, allowing the multi-antenna UAV to provide services to multiple GUIs simultaneously. Then, by jointly optimizing the UAV's flight trajectory, flight time, and transmit beamforming, an optimization problem is constructed with minimizing the UAV's total energy consumption as the optimization goal, while meeting the user's QoS requirements. This optimization problem is then solved. To solve this optimization problem, a path discretization method combined with a golden section search method is first used to determine the UAV's flight time and flight trajectory, thereby minimizing the UAV's propulsion energy consumption. Then, the successive convex approximation (SCA) technique is used to further minimize the UAV's communication energy consumption. Simulation results show that this method and system significantly outperform existing benchmark solutions in terms of energy consumption, demonstrating high efficiency and practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a method for minimizing energy consumption in multi-antenna UAV communications provided by an embodiment of the present invention;

[0037] Figure 2 is an optimal flight trajectory diagram of the UAV provided by an embodiment of the present invention;

[0038] Figure 3 is a relationship diagram between the UAV flight speed and the UAV propulsion energy consumption in the golden section search method provided by an embodiment of the present invention;

[0039] Figure 4 is a relationship diagram between the optimized UAV flight speed and flight time provided by an embodiment of the present invention;

[0040] Figure 5 is a graph showing the relationship between UAV propulsion power and flight speed provided by an embodiment of the present invention;

[0041] Figure 6 The embodiment of the present invention provides different R min Comparison of communication energy consumption under different conditions;

[0042] Figure 7 This is a comparison chart of communication energy consumption under different user numbers provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and are not to be construed as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of protection of the present invention. Many changes may be made to the present invention without departing from the spirit and scope of the present invention.

[0044] The embodiment of the present invention first provides a method for minimizing energy consumption of multi-antenna UAV communication, such as Figure 1 As shown, the steps include:

[0045] Build an aerial video surveillance system based on a multi-antenna rotorcraft drone;

[0046] With the goal of minimizing the total energy consumption of the UAV, under the constraint of ensuring that each ground user can achieve the minimum acceptable playback rate, under the constraints of the UAV's starting and ending positions, and under the UAV's flight speed not exceeding its maximum speed V max Under the constraint of , we construct an optimization problem of jointly optimizing the UAV’s transmit beamforming, trajectory, and flight time T;

[0047] Solve the optimization problem to obtain the trajectory, transmit beamforming and flight time T of the UAV.

[0048] The following describes each step in more detail.

[0049] (1) System model

[0050] To achieve high-performance wireless communications using multi-antenna UAVs, the UAVs can be equipped with multiple antennas. By utilizing spatial multiplexing and multi-antenna beamforming techniques, interference between users on the same frequency resource can be effectively eliminated, enabling simultaneous communication between the UAV and multiple GUs. This approach can optimize the performance of wireless communication systems, thereby improving the efficiency and quality of video transmission.

[0051] This embodiment provides an aerial video surveillance system based on a multi-antenna rotor UAV. The system consists of K single-antenna GUs and a M x ×M y =M≥2 Uniform Rectangular Array (URA) antennas. Equipped with a camera, the UAV takes off from a starting point and flies to a destination, capturing video during flight. It then uses multi-antenna beamforming technology to transmit the video signal to K single-antenna users.

[0052] To reduce the energy consumption caused by the frequent ascent and descent of the UAV, it is assumed that the flight altitude of the UAV remains fixed and is set to a constant value H0. The total running time of the UAV is used as the optimization variable, denoted as T. At any time t∈[0,T], the three-dimensional position and velocity of the UAV can be expressed as x u (t),y u (t) represents the horizontal and vertical coordinates of the u-th UAV (UAVu), and the speed of the UAV is recorded as represents the first-order derivative of the position vector q(t) with respect to time t, i.e., the instantaneous velocity of the UAV, where [·] T Represents the transpose operation of the matrix. The initial position (specify the starting point) and destination (specify the end point) of the UAV are predetermined and are represented by q I and q F Indicates. Define the coordinates of user k as u k =[x k ,y k ,0] T (height is 0), the three-dimensional distance between UAV u and user k is represented by d k (t)=‖q(t)-u k ‖,‖ ‖ represents the modular operation, and the horizontal distance between UAV u and user k is expressed as

[0053] Since the UAV is at a high altitude, the air-to-ground communication channel needs to consider not only the direct path, but also multiple reflections and scattering from surrounding buildings and objects. Therefore, the Racian channel model that includes Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) multipath components is adopted as follows:

[0054]

[0055] Among them, K R represents the ratio of the LoS path power to the NLoS path power, α1 is the path loss index of the link between the UAV and the GU, is the wavelength, c is the speed of light, fc is the frequency, The contribution of the LoS path can be expressed as:

[0056]

[0057] in, represents the Kronecker product between matrices, Indicates the distance between antenna units; φ k (t) and θ k(t) represent the horizontal and vertical arrival angles of UAV to GU, respectively. [·] H Represents the conjugate transpose operation of a matrix.

[0058] also, reflects the multipath components of the NLoS path. In particular, where n k,m (t) and are independent and identically distributed circularly symmetric complex Gaussian (CSCG) distributed variables. These variables represent the small-scale fading of the NLoS path and have zero mean and unit variance.

[0059] (2) Video Stream Model

[0060] In this embodiment, the beamforming vector assigned by the UAV to user k at time t is expressed as

[0061] Therefore, the signal-to-noise ratio (SINR) of user k at time t can be expressed as:

[0062]

[0063] in, is the background noise power of each user. Assuming that each user can directly play the received video data, the video playback rate that user k can achieve at time t can be expressed as:

[0064]

[0065] In order to ensure the QoS requirements of users, this embodiment needs to set a lower limit to ensure that the video playback rate is higher than this lower limit R min , that is, the following conditions are met:

[0066] B log2(1+SINR k (t))≥R min

[0067] B represents the channel bandwidth.

[0068] (3) Energy model

[0069] The limited battery capacity of UAVs limits their flight time and transmission capabilities. Therefore, effectively managing energy consumption is crucial for aerial video transmission. This example focuses on the energy utilization of rotary-wing UAVs during flight propulsion and communication.

[0070] This example assumes that the UAV is operating at a fixed altitude. This assumption allows this example to ignore the energy consumption associated with vertical motion and focus only on the energy consumption of horizontal flight. At time t, the UAV's propulsion power can be expressed as:

[0071]

[0072] in It represents the basic power consumption of UAV to overcome air resistance. represents the driving power consumption of the rotor, represents the induced power consumption of the rotor. v0, Ω, r represent the induced average speed of the rotor, the angular velocity of the rotor blades, and the radius of the rotor, respectively. P0 and P i Respectively represent the blade power and induced power in the hovering state. r ,ρ represent the fuselage drag ratio, rotor robustness, rotor disc area and air density respectively.

[0073] In addition, the transmission power of the UAV at time t can be expressed as: Therefore, the total energy consumption of UAV can be expressed as:

[0074] E total =∫0 T (P fly (t)+P com (t))dt

[0075] (4) Optimization problem model

[0076] This embodiment studies the challenge of how to minimize the total energy consumption of UAVs during mission execution. Specifically, the UAV needs to start from a specified starting point and fly to a specified destination, and provide video transmission services to users during the process. To this end, this embodiment studies the flight trajectory {q(t)}, transmit beamforming {w k (t)} and the task completion time T are optimized. The main goal of this embodiment is to reduce energy consumption as much as possible while ensuring user QoS. To solve this problem, the following optimization problem is constructed:

[0077] (P1):

[0078] stC1:q(0)=q I ,q(T)=q F ,

[0079]

[0080] The constraint C1 in problem (P1) specifies the starting and ending positions of the UAV, and the constraint C2 limits its speed, where V max represents the maximum speed of the UAV. Constraint C3 ensures that each GU can achieve the minimum acceptable playback rate.

[0081] It can be verified that problem (P1) is a non-convex optimization problem and faces the following challenges: First, the objective function is non-convex and contains the optimization variable T as the upper limit of the integral. Second, the trajectory of the UAV and the optimization variable of the transmit beamforming are in the SINR k (t) is tightly coupled. To address these challenges, this embodiment proposes a two-stage optimization algorithm. Since propulsion energy consumption is the main component of UAV energy use, the primary goal of this embodiment is to minimize this part of energy consumption. In the first stage, given the UAV's transmit beamforming {w k (t)}, this embodiment minimizes propulsion energy consumption by jointly optimizing the UAV trajectory {q(t)} and flight time T. In the second stage, this embodiment further optimizes the UAV's transmit beamforming {w k (t)}, to minimize the communication energy consumption.

[0082] In the first stage, in order to minimize the propulsion energy consumption of the UAV, the optimization problem (P1) can be transformed into the first sub-problem:

[0083] (P2):

[0084] stC1,C2

[0085] Since q(t) is a continuous variable that is tightly coupled to T, solving this problem directly is quite challenging. To address this problem, this embodiment uses a path discretization method to divide the UAV's flight trajectory into L segments, thereby obtaining L+1 path points.

[0086] The coordinates of the UAV at the beginning of the lth segment are The duration of each segment is represented by δ l The end of the lth path is also the beginning of the l+1th path, which is denoted by q l+1 Therefore, the flight time T of the UAV can be expressed as Assume ||q l+1 -q l ||≤d max , where d max =V max δ l is small enough to approximate the path as a straight line. In each segment of the path, the velocity v lis considered constant, The propulsion power of the UAV when flying along the lth path can be expressed as:

[0087]

[0088] In this embodiment, V l =||v l || is the flight speed of the UAV along the lth path, and its flight energy consumption on this path can be expressed as Given q l and q l+1 , this embodiment considers how to minimize the propulsion energy consumption of the UAV along the first segment of the path, and thus the optimization problem (P2) can be transformed into the second sub-problem:

[0089] (P3):

[0090] stC1,

[0091] C4: 0≤V l ≤V max ,

[0092] C5: ||q l+1 -q l ||≤d max

[0093] It should be noted that the optimization goal of problem (P3) is about V l To this end, this embodiment adopts the golden section search method, which is an interval contraction method that finds the optimal solution by gradually reducing the interval containing the optimal solution until the length of the interval reaches a minimum.

[0094] To find the minimum of the function f(x) within the interval [a, b], two points x1 and x2 can be randomly selected within the interval. By comparing the function value or derivative of f(x) at these two points, a portion of the interval [a, x1] or [x2, b] can be eliminated, thereby shortening the search interval. This process is iterated until the interval shrinks to a single point or its length is less than a given precision. The golden section search, also known as the 0.618 method, is based on the principle of continuously reducing the length of the interval to find the minimum of the objective function. This method selects new points based on 0.618 (and 0.382) of the total length of the feasible region.

[0095] Specifically, this example uses the golden section search method to obtain the optimal speed for each path segment (this example is called Algorithm 1). The specific steps are as follows:

[0096] 1) Let x represent the UAV speed V l , then the function f(x) represents According to constraint C4, the initial interval of x [a,b] is [0,V max ];

[0097] 2) Using the golden ratio, calculate x1 = a + 0.382(ba) and x2 = a + b – x1;

[0098] 3) Calculate the function values ​​f1 = f(x1) and f2 = f(x2) at points x1 and x2;

[0099] 4) Determine whether f1 is greater than f2. If so, set a = x1, x2 = a + b - x1, and return to step 3);

[0100] If not, set b = x2, x1 = a + b - x2 and return to step 3);

[0101] 5) When the interval [a, b] is less than the specified precision ∈ 1, end the iteration of steps 3) and 4) and output the current interval [a, b];

[0102] 6) Determine the optimal speed that minimizes the flight energy consumption of the UAV in each path and select it arbitrarily in the interval [a, b].

[0103] Next, the optimal flight trajectory of the UAV is determined.

[0104] The UAV's curved path can be approximated by dividing it into many small straight line segments. In the first segment, the UAV's flight energy consumption is: The total flight energy consumption for the entire path can be expressed as The optimal speed of any straight line segment l obtained by the golden section search method is expressed as V l * , and V * =V l * , that is, the optimal speed of each line segment is the same and has nothing to do with the starting point and the end point. Therefore, the total flight energy can be expressed as: in Indicates q I With q F Therefore, the flight energy consumption is proportional to the flight distance, and q I With q F The shortest path between q is a straight line. Therefore, the UAV should fly along a straight line at a constant speed V*. That is, in order to achieve the minimum propulsion energy consumption, the UAV must fly in a straight line at a constant speed between the given starting point and the end point. Therefore, it can be determined that the UAV flies from q I to q F The optimal flight trajectory is determined to minimize propulsion energy consumption.

[0105] After optimizing the UAV's propulsion energy consumption, we determined that its optimal flight path is a straight line and that the optimal flight speed can be obtained using the golden section search method. Next, we use these results to minimize communication energy consumption. The communication energy optimization problem for UAV video transmission can be transformed from problem (P1) into the third subproblem, as follows:

[0106] (P4):

[0107]

[0108] SINR k(l) represents the signal-to-noise ratio of user k in segment l. Note that problem (P4) is equivalent to optimizing each time segment independently, because its constraints are not coupled across segments and the objective function can be decomposed into independent time segments. Therefore, we only need to focus on minimizing the UAV's communication energy consumption in a single time segment, thereby omitting the segment index and further transforming the optimization problem (P4) into the fourth subproblem:

[0109] (P5):

[0110]

[0111] w k 、SINR k Represents w in a single time period k (t), SINR k(l) .

[0112] Constraint C7 can be transformed into:

[0113]

[0114] h k is the channel gain vector corresponding to user k. Then problem (P5) can be reformulated as:

[0115] (P6):

[0116]

[0117] The left side of the constraint in problem (P6) contains a non-concave function term |h i w i | 2 , so that the problem is still a non-convex optimization problem. To solve this problem, this embodiment uses SCA technology. Specifically, by Upper pair|h i wi | 2 Performing a first-order Taylor expansion, this embodiment obtains:

[0118]

[0119] Re{} represents the real part operation.

[0120] By adding |h i w i | 2 Replacing it with its lower bound Di, this embodiment performs a convex approximation on problem (P6) and obtains the fifth sub-problem:

[0121] (P7):

[0122]

[0123] It can be shown that problem (P7) is a convex optimization problem and can be solved by standard convex optimization techniques, which we call Algorithm 2 in this example. The solution steps are:

[0124] 1) Initialize the iteration count r = 0;

[0125] 2) In each iteration, the convex optimization problem (P7) is solved using the CVX toolkit to obtain the optimal solution. Then update After r=r+1, enter the next iteration;

[0126] 3) When the solution converges to the specified precision ∈ 2, the iteration stops and the current optimal solution is output.

[0127] Corresponding to the above method, an embodiment of the present invention also provides a multi-antenna UAV communication energy consumption minimization system, which includes an intelligent agent, and the intelligent agent is used to execute the multi-antenna UAV communication energy consumption minimization method.

[0128] In order to verify the effectiveness and performance of the proposed method and system, this embodiment uses the CVX toolkit and its SeDuMi solver in Matlab R2022b to perform comprehensive simulations under various parameter settings.

[0129] Assume that the rotary wing UAV is equipped with x ×M y =Antenna array composed of M units, where M x =5,M y = 2. The initial position of the UAV is set to q I =[0,0,H0] T , the final position is set to q F =[180,60,H0] TFlight altitude H0 = 100m, maximum speed V max =50m / s. In terms of energy consumption settings, this embodiment uses the following parameters: gravitational acceleration g = 9.8m / s 2 , rotor induced average speed v0 = 4.03m / s, rotor blade angular velocity Ω = 300 rad / s, rotor radius r = 0.4m. The blade power during hovering P0 = 79.86W, the induced power during hovering P i = 88.63 W. Additional parameters include fuselage drag ratio d0 = 0.3, rotor robustness s = 0.05 m 3 , rotor disk area A r =0.503m 2 , air density ρ=1.225kg / m 3 The channel-related parameters are set as follows: path loss index α1 = 3.5, Rician channel factor K R =20dB, carrier frequency f c =2.4GHz, carrier wavelength θ = c / f c (where c is the speed of light), the antenna unit spacing d a =λ / 2, bandwidth B = 0.1MHz, noise power spectrum density 150dBm / Hz. Finally, given the communication energy consumption and algorithm design related parameters are ∈1=10 -2 ,∈2=10 -3 .

[0130] The optimal flight trajectory of the UAV obtained by the experiment is as follows: Figure 2 As shown in Figure 2, the UAV flies from the starting point to the end point at a constant linear speed. This trajectory verifies the theoretical prediction that the most energy-efficient path is a straight line between two points. By solving problem (P3), this embodiment derives the relationship between the UAV propulsion energy consumption and flight speed, as shown in Figure 2. Figure 3 shown. Figure 3 The energy consumption of the UAV at different speeds is shown, showing the propulsion energy required at different speeds. Using Algorithm 1, the optimal speed with the lowest energy consumption, v0 = 18.2217 m / s, corresponds to a minimum propulsion energy of 28.1579 Joules.

[0131] Figure 4 and Figure 5 The relationship between the optimized UAV propulsion power, flight speed and flight time is shown respectively. Figure 4 and Figure 5As can be seen, Algorithm 1 successfully achieves a balance between propulsion power and flight time and speed, thereby significantly saving energy. Specifically, although the UAV's flight speed is lower under this algorithm, resulting in a longer flight time, its overall propulsion energy consumption is significantly reduced compared to the "UAV operation time minimization scheme." In contrast, although the "energy-saving scheme" exhibits the lowest propulsion power and speed, due to its longer flight time, although it reduces instantaneous energy consumption, it may not be suitable for scenarios with high requirements for mission time. Overall, Algorithm 1 finds an effective balance between energy consumption and flight time, meeting energy-saving requirements while completing the mission relatively quickly, thus demonstrating excellent performance in both energy consumption and time efficiency.

[0132] This example uses Algorithm 2 (a multi-antenna solution) to optimize the energy consumption of multi-antenna UAV video communications. To verify the effectiveness of this algorithm, this example compares it with three baseline solutions: a single-antenna solution, a maximum ratio combining (MRC) beamforming solution, and a constrained multi-antenna solution. Figure 6 and Figure 7 The communication energy consumption performance of each scheme under different minimum video rates and different numbers of users is shown respectively. Figure 6 and Figure 7 The results show that in single-antenna and MRC beamforming schemes, the UAV fails to fully utilize spatial multiplexing technology, resulting in a significant increase in energy consumption under multi-user and high transmission rate conditions. In contrast, multi-antenna UAVs using beamforming technology can more accurately focus the signal on the user's receiving antenna. This approach significantly improves the received signal strength and reduces the signal power required by the transmitter, thereby effectively reducing energy consumption. Although the restricted multi-antenna scheme improves compared to the single-antenna scheme in multi-user scenarios, its energy saving effect does not reach the level of the full multi-antenna scheme due to configuration limitations. The full multi-antenna scheme achieves more efficient signal distribution through more flexible antenna control, further reducing communication energy consumption while ensuring user QoS.

[0133] Experimental results demonstrate that the comprehensive multi-antenna solution significantly reduces communication energy consumption while ensuring QoS for video users. By optimizing the UAV's trajectory and flight speed and more efficiently allocating signal energy, the solution achieves a significant balance between energy consumption and mission efficiency, providing strong support for energy-efficient applications of UAVs connected in future cellular networks.

[0134] An embodiment of the present invention provides a method and system for minimizing the energy consumption of multi-antenna UAV communications. First, an aerial video surveillance system based on a multi-antenna rotorcraft UAV is constructed, allowing the multi-antenna UAV to provide services to multiple GUIs simultaneously. Then, by jointly optimizing the UAV's flight trajectory, flight time, and transmit beamforming, while meeting the user's QoS requirements, an optimization problem is constructed with minimizing the UAV's total energy consumption as the optimization goal, and the optimization problem is further solved. To solve this optimization problem, a path discretization method is first used in combination with a golden section search method to determine the UAV's flight time and flight trajectory, thereby minimizing the UAV's propulsion energy consumption. Then, a successive convex approximation technique is used to further minimize the UAV's communication energy consumption. Simulation results show that this method and system significantly outperform existing benchmark solutions in terms of energy consumption, demonstrating high efficiency and practicality.

[0135] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for minimizing energy consumption in multi-antenna UAV communication, characterized in that: Including steps: Construct an aerial video surveillance system based on a multi-antenna rotor UAV; the aerial video surveillance system consists of A single-antenna ground user and an aircraft equipped The drone is composed of a rotor drone with a uniform rectangular array antenna. The drone takes off from a designated starting point and flies to a designated destination at a fixed altitude. During the flight, it captures video and provides video transmission services to users. With the goal of minimizing the total energy consumption of the UAV, under the constraint of ensuring that each ground user can achieve the minimum acceptable playback rate, under the constraints of the UAV's starting and ending positions, and under the UAV's flight speed not exceeding its maximum speed Under the constraints of Optimization problem; Solve the optimization problem to obtain the trajectory, transmit beamforming and flight time of the UAV ; In the process of solving the optimization problem, the golden section search method is used to obtain the optimal speed of each path segment, which specifically includes the following steps: 1) Representative speed , with the function Representing drones in Flight loss on segment path ,Sure The initial interval for ; 2) Calculate using the golden ratio and ; 3) Calculation 、 Function value at point 、 ; 4) Judgment Is it greater than If so, then , and return to step 3); if otherwise, let and return to step 3); 5) When the interval Less than the specified accuracy When , the iteration of steps 3) and 4) ends and the current interval is output ; 6) Determine the optimal speed in the interval that minimizes the energy consumption of the drone in each path Select any one of them.

2. The method for minimizing energy consumption of multi-antenna UAV communication according to claim 1, characterized in that: The total energy consumption of the drone is equal to the drone's Propulsion power and transmit power The sum of the flight time The integral within; the drone at time Propulsion power Equal to the drone at time The sum of the basic power consumption consumed to overcome air resistance, the driving power consumption of the rotor, and the induced power consumption of the rotor; Transmit power at the moment Equal to assigned to The sum of the squares of the moduli of the beamforming vectors of the terrestrial users.

3. The method for minimizing energy consumption of multi-antenna UAV communications according to claim 2, characterized in that: Drones at all times The basic power consumption to overcome air resistance is equal to , drones at all times The driving power consumption of the rotor is equal to , drones at all times The induced power consumption of the rotor is equal to , represent the induced mean speed of the rotor, the angular velocity of the rotor blades, and the radius of the rotor, respectively. and represent the blade profile power and induced power in the hovering state, are the fuselage drag ratio, rotor robustness, rotor disc area and air density, respectively. Indicates that the drone is at time The velocity vector, Represents the modulo operation on a vector.

4. The method for minimizing energy consumption of multi-antenna UAV communication according to claim 1, characterized in that: The limit on the minimum acceptable broadcast rate that each terrestrial user can achieve is expressed as the channel bandwidth and at the moment Ground users Achievable video playback rate The product of is not less than the minimum acceptable playback rate , equal , Indicates ground users At the moment The signal-to-noise ratio of When , a Racian channel model including line-of-sight and non-line-of-sight multipath components is adopted.

5. The method for minimizing energy consumption of multi-antenna UAV communication according to claim 4, characterized in that: Solving the optimization problem specifically includes the following steps: Transmit beamforming for a given drone , transforming the optimization problem into a joint optimization of the UAV trajectory and flight time The first sub-problem of The path discretization method is used to divide the flight trajectory of the UAV into segment, thus obtaining path points; consider each path as a straight line, assuming that each path Velocity vector of the drone Considered as constant, given the UAV in The coordinates of the start and end of the segment path and , transforming the first sub-problem into the second sub-problem, which is to minimize the UAV's The propulsion energy consumption of the segment path is the target, 、 Optimize the duration of each path for the constraints , Indicates the maximum length of any path allowed. For drones along the The flight speed of the segment path; Solve the second sub-problem and get the duration , and further obtain the flight time , and the optimal flight trajectory of the drone ; Substitute the obtained flight time and the optimal flight trajectory of the drone , transforming the optimization problem into minimizing As the goal, Optimizing UAV Transmit Beamforming for Constrained Conditions The third sub-problem is Indicates ground users The signal-to-noise ratio on line segment l; Solve the third sub-problem to obtain the optimal UAV transmit beamforming .

6. The method for minimizing energy consumption of multi-antenna UAV communication according to claim 1, characterized in that: The optimal flight trajectory of a drone is to fly in a straight line at a constant speed between a specified starting point and a specified end point.

7. The method for minimizing energy consumption of multi-antenna UAV communication according to claim 5, characterized in that: Solve the third sub-problem to obtain the optimal UAV transmit beamforming , specifically including the steps: The third subproblem is transformed into minimizing As the goal, Optimize for constraints The fourth sub-problem of 、 Represents a single time period 、 ; The constraints Transformed into , For users The corresponding channel gain vector, , then Replace with its lower bound , and obtain new constraints , represents the real part operation, express Any feasible point of , thereby transforming the fourth sub-problem into the fifth sub-problem; Solve the fifth sub-problem and get the optimal solution .

8. The method for minimizing energy consumption of multi-antenna UAV communications according to claim 7, characterized in that: The fifth sub-problem is solved using a convex optimization problem solving tool.

9. A multi-antenna UAV communication energy consumption minimization system, characterized by: It includes an intelligent agent, which is used to execute the multi-antenna unmanned aerial vehicle communication energy consumption minimization method described in any one of claims 1 to 8.

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