Method and system for minimizing communication energy consumption of multi-antenna unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle communication, and particularly discloses a multi-antenna unmanned aerial vehicle communication energy consumption minimization method and system.The method comprises the steps that firstly, an aerial video monitoring system based on a multi-antenna rotor unmanned aerial vehicle is constructed, and a multi-antenna unmanned aerial vehicle (UAV) is allowed to provide services for a plurality of ground users (GU) at the same time; then, through joint optimization of the flight path, the flight time and transmitted beam forming of the UAV, on the premise that the QoS requirement of a user is met, an optimization problem is constructed by taking minimization of the total energy consumption of the UAV as an optimization target, and the optimization problem is further solved. In order to solve the optimization problem, firstly, a path discretization method is combined with a golden section search method to determine the flight time and the flight path of the UAV, so that the propulsion energy consumption of the UAV is minimized, and then the communication energy consumption of the UAV is minimized. Simulation results show that the method and the system are remarkably superior to an existing reference scheme in the aspect of energy consumption, and high efficiency and practicability are shown.

Description

Technical Field

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

[0002] Unmanned Aerial Vehicle (UAV) is often used as an aerial base station due to its high maneuverability and flexible deployment capabilities to establish high-quality wireless connections with ground users (GU). Compared with traditional ground networks, UAVs can provide better communication link quality and provide on-demand and timely services based on user requests. UAVs have a wide range of applications, including natural gas pipeline inspections, emergency rescue operations, fire monitoring, etc. These scenarios require UAVs and GUs to form an efficient video transmission network. In addition, in urban safety inspections, UAVs must be able to accurately fly and hover in narrow spaces to provide real-time video monitoring services for emergency situations such as fires, traffic accidents or public safety incidents. Compared with fixed-wing UAVs, rotary-wing UAVs are more popular due to their excellent maneuverability and flexibility, and can perform precise operations and stable flight in limited spaces, which makes them particularly suitable for tasks that require fast 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 UAV-assisted communication scheme has been proposed 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 mainly focuses on single-antenna UAVs, a configuration that enables UAVs to communicate with only one GU at a time, limiting their ability to communicate with multiple GUs simultaneously. This limitation results in reduced system throughput and increased latency in video transmission.

[0005] On the other hand, the capacity of UAV onboard batteries is limited by its size and load capacity, which restricts the sustainability of UAV flight. Especially for rotary-wing UAVs, their battery life is usually short. When transmitting video, battery consumption increases rapidly, which limits the flight time and distance, and has an adverse impact on long-term monitoring tasks. Therefore, reducing the total energy consumption of UAVs while ensuring the quality of video streaming services becomes a key challenge.

[0006] Several studies have explored various issues of UAV energy consumption. 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 indicator for measuring video transmission performance. How to minimize the total energy consumption of UAV 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, and solves the technical problem 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] In order to solve the above technical problems, the present invention provides a method for minimizing energy consumption of multi-antenna UAV communication, comprising the steps of:

[0009] Construct 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 end point at a fixed altitude. During the flight, it captures video and provides video transmission services for 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 starting and ending positions of the UAV, and under the constraints of the UAV's flight speed not exceeding its maximum speed V max Under the constraint of , the optimization problem of jointly optimizing the transmission beamforming, trajectory and flight time T of the UAV is constructed;

[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 air resistance at time t, the driving power consumption of the rotor, and the induced power consumption of the rotor; the transmitting power P of the UAV 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 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 denote the blade 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 ground 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 steps of:

[0016] Given the transmit beamforming of the UAV {w k (t)}, transforming the optimization problem into the first sub-problem of jointly optimizing the trajectory {q(t)} and the 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 the velocity vector v of the UAV in each path l is assumed to be 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 path and takes ||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] Substituting the obtained flight time T and the optimal flight trajectory {q(t)} of the UAV, the optimization problem is transformed into minimizing As the target, B log2(1+SINR k(l) )≥R min Optimizing the transmit beamforming of a UAV for constraints {w k (t)}, SINR k(l) represents the signal-to-noise ratio of ground user k on line segment l;

[0020] Solve the third sub-problem to 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 drone on the lth path Determine the initial interval of x [a,b] 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 UAV 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 To optimize the constraints {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 for multiple GUs at the same time. Then, by jointly optimizing the flight trajectory, flight time and transmit beamforming of the UAV, an optimization problem is constructed with minimizing the total energy consumption of the UAV as the optimization goal under the premise of meeting the user's QoS requirements, and the optimization problem is further solved. In order to solve the optimization problem, the path discretization method is first combined with the golden section search method to determine the flight time and flight trajectory of the UAV, thereby minimizing the propulsion energy consumption of the UAV, and then the successive convex approximation (SCA) technology is used to further minimize the communication energy consumption of the UAV. The simulation results show that the method and system are significantly better than the existing benchmark scheme in terms of energy consumption, showing high efficiency and practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 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 the flight time provided by an embodiment of the present invention;

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

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

[0042] Figure 7 It 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 specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood 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 patent protection of the present invention, because many changes can 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 rotary-wing 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 starting and ending positions of the UAV, and under the constraints of the UAV's flight speed not exceeding its maximum speed V max Under the constraint of , the optimization problem of jointly optimizing the transmission beamforming, trajectory and flight time T of the UAV is constructed;

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

[0048] Each step is described in more detail below.

[0049] (1) System model

[0050] In order to apply multi-antenna UAV for high-performance wireless communication, UAV can be equipped with multiple antennas. By utilizing spatial multiplexing and multi-antenna beamforming technology, interference between users on the same frequency resource can be effectively eliminated, and simultaneous communication between UAV and multiple GUs can be achieved. This method can optimize the performance of the wireless communication system, 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. The UAV is equipped with a camera, takes off from a starting point and flies to a destination, capturing video during the flight. It then uses multi-antenna beamforming technology to transmit the video signal to K single-antenna users.

[0052] In order to reduce the energy consumption caused by the frequent rise and fall 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 taken 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 drone), where [·] T represents the transpose operation of the matrix. The initial position (specified starting point) and destination (specified 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 denoted as 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 containing 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 UAV and GU, is the wavelength, c is the speed of light, fc is the frequency, represents the contribution of the LoS path, which can be expressed as:

[0056]

[0057] in, represents the Kronecker product between matrices, Represents the distance between antenna units; φ k (t) and θ k(t) represent the horizontal and vertical arrival angles from UAV to GU, that is, [·] 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 NLoS paths 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 further 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, how to effectively manage energy consumption is crucial for aerial video transmission. This embodiment mainly discusses the energy utilization of rotary-wing UAVs during flight propulsion and communication.

[0070] This embodiment assumes that the UAV is operating at a fixed altitude. This assumption enables this embodiment to ignore the energy consumption associated with vertical motion and only focus on the energy consumption of horizontal flight. At time t, the propulsion power of the UAV 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. d0,s,A 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 UAV during mission execution. Specifically, the UAV needs to start from a specified starting point, fly to a specified end point, and provide video transmission services to users during the process. To this end, this embodiment performs flight trajectory {q(t)}, transmit beamforming {w k (t)} and 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. In order 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 variables of the transmit beamforming are in the SINR k (t). 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 jointly optimizes the trajectory {q(t)} and the flight time T of the UAV to minimize the propulsion energy consumption. In the second stage, this embodiment further optimizes the 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, it is quite challenging to solve this problem directly. To solve this problem, this embodiment adopts a path discretization method to divide the flight trajectory of the UAV into L segments, thereby obtaining L+1 path points.

[0086] The coordinates of the UAV at the beginning of the lth path are The duration of each segment is represented by δ l If , then the end of the lth path is also the beginning of the l+1th path, 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 lth path, that is, 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, the present embodiment adopts the golden section search method, which is an interval contraction method, which finds the optimal solution by gradually reducing the interval containing the optimal solution until the length of the interval reaches the minimum value.

[0094] In order to find the minimum value of the function f(x) in the interval [a,b], two points x1 and x2 can be arbitrarily selected in the interval. By comparing the function value or derivative value of f(x) at these two points, a part of the interval [a,x1] or [x2,b] can be eliminated, thereby shortening the length of the search interval. This process is iterated continuously until the interval shrinks to a point or its length is less than the 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 value of the objective function. This method selects new points based on 0.618 (and 0.382) of the total length of the feasible domain.

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

[0096] 1) x represents 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 otherwise, let 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 curved path of the UAV can be approximated by dividing it into many small straight line segments. In the first segment, the flight energy consumption of the UAV is: The total flight energy consumption of 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 the starting point and the end point 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 propulsion energy consumption of the UAV, it is determined that its optimal flight path is a straight line and the optimal flight speed can be obtained by the golden section search method. The results obtained are used below to minimize the communication energy consumption. The communication energy consumption optimization problem of UAV video transmission can be transformed from problem (P1) to obtain the third sub-problem, as follows:

[0106] (P4):

[0107]

[0108] SINR k(l) represents the signal-to-noise ratio of user k on 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 communication energy consumption of the UAV in a single time segment, thereby omitting the segment index and further transforming the optimization problem (P4) into the fourth sub-problem:

[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 restated 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 proved that problem (P7) is a convex optimization problem and can be solved by standard convex optimization techniques, which is called Algorithm 2 in this example. The solution steps are:

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

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

[0126] 3) When the specified accuracy ∈2 is reached, 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 consisting 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] T. Flight 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 radians / 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 disc area A r =0.503m 2 , air density ρ = 1.225 kg / m 3 The channel related parameters are set as: 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 experiment is as follows: Figure 2 As shown in Figure 2, the UAV flies from the starting point to the end point at a uniform 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 the flight speed, as shown in Figure 2. Figure 3 shown. Figure 3 The energy consumption of the UAV at different speeds is shown, and the propulsion energy required at different speeds is shown. The optimal speed with the lowest energy consumption, v0 = 18.2217 m / s, is determined using Algorithm 1, and the corresponding minimum propulsion energy is 28.1579 Joules.

[0131] Figure 4 and Figure 5 The relationship between the optimized UAV propulsion power and the flight speed and flight time are shown respectively. Figure 4 and Figure 5It can be seen that Algorithm 1 successfully achieves a balance between propulsion power and flight time and speed, thereby significantly saving energy. Specifically, although the flight speed of the UAV is relatively low 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" shows the lowest propulsion power and speed, due to the long flight time, although the instantaneous energy consumption is reduced, it may not be suitable for scenarios with high requirements on mission time. Overall, Algorithm 1 finds an effective balance between energy consumption and flight time, which not only meets the energy-saving needs, but also completes the task quickly, thus showing excellent performance in both energy consumption and time efficiency.

[0132] This embodiment uses Algorithm 2 (multi-antenna scheme) to optimize the energy consumption of multi-antenna UAV video communication. To verify the effectiveness of the algorithm, this embodiment compares it with three benchmark schemes: a single antenna scheme, a maximum ratio combining (MRC) beamforming scheme, and a constrained multi-antenna scheme. Figure 6 and Figure 7 The communication energy consumption performance of each scheme is shown under different minimum video rates and different numbers of users. Figure 6 and Figure 7 It is shown that in the single-antenna scheme and the MRC beamforming scheme, 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, the multi-antenna UAV 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 is an improvement over the single-antenna scheme in multi-user scenarios, its energy-saving effect does not reach the level of the complete multi-antenna scheme due to configuration limitations. The complete 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 show that the complete multi-antenna solution significantly reduces communication energy consumption while ensuring QoS for video users. By optimizing the trajectory and flight speed of the UAV and allocating signal energy more efficiently, the solution achieves a significant balance between energy consumption and task completion efficiency, providing strong support for energy-saving applications of UAVs connected in future cellular networks.

[0134] The embodiment of 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 for multiple GUs at the same time. Then, by jointly optimizing the flight trajectory, flight time and transmit beamforming of the UAV, an optimization problem is constructed with minimizing the total energy consumption of the UAV as the optimization goal while meeting the user's QoS requirements, and the optimization problem is further solved. In order to solve the optimization problem, the path discretization method is first combined with the golden section search method to determine the flight time and flight trajectory of the UAV, thereby minimizing the propulsion energy consumption of the UAV, and then the successive convex approximation technology is used to further minimize the communication energy consumption of the UAV. The simulation results show that the method and system are significantly better than the existing benchmark solutions in terms of energy consumption, showing 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 equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for minimizing energy consumption in multi-antenna UAV communication, characterized in that: Includes steps: Construct 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 end point at a fixed altitude. During the flight, it captures video and provides video transmission services for 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 starting and ending positions of the UAV, and under the constraints of the UAV's flight speed not exceeding its maximum speed V max Under the constraint of , the optimization problem of jointly optimizing the transmission beamforming, trajectory and flight time T of the UAV is constructed; Solve the optimization problem to obtain the trajectory, transmit beamforming and flight time T of the UAV.

2. A method for minimizing energy consumption of multi-antenna UAV communication according to claim 1, characterized in that: 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 air resistance at time t, the driving power consumption of the rotor, and the induced power consumption of the rotor; the transmitting power P of the UAV 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.

3. The method for minimizing energy consumption of multi-antenna UAV communication according to claim 2, characterized in that: The basic power consumption of the drone to overcome 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 denote the blade 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.

4. The method for minimizing energy consumption of multi-antenna UAV communication according to claim 1, characterized in that: The limit of 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 ground 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.

5. A 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: Given the transmit beamforming of the UAV {w k (t)}, transforming the optimization problem into the first sub-problem of jointly optimizing the trajectory {q(t)} and the flight time T of the UAV; The flight trajectory of the UAV is divided into L segments using the path discretization method, thus obtaining L+1 path points; Consider each path as a straight line and assume that the velocity vector v of the drone 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 path and takes ||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; 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; Substituting the obtained flight time T and the optimal flight trajectory {q(t)} of the UAV, the optimization problem is transformed into minimizing As the target, B log2(1+SINR k(l) )≥R min Optimizing the transmit beamforming of a UAV for constraints {w k (t)}, SINR k(l) represents the signal-to-noise ratio of the ground user k in the line segment; Solve the third sub-problem to obtain the optimal UAV transmit beamforming {w k (t)}.

6. A method for minimizing energy consumption of multi-antenna UAV communication according to claim 5, characterized in that: 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: 1) Let x represent the speed V l , the function f(x) represents the flight loss of the drone on the lth path Determine the initial interval [a, b] of x as [0, V max ]; 2) Using the golden ratio, calculate x1 = a + 0.382 (ba) and x2 = a + b - x1; 3) Calculate the function values ​​f1=f(x1) and f2=f(x2) at points x1 and x2; 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); 5) When the interval [a, b] is less than the specified precision ∈ 1, the iteration of steps 3) and 4) is terminated, and the current interval [a, b] is output; 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].

7. A method for minimizing energy consumption of multi-antenna UAV communication according to claim 6, 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.

8. 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 {w k (t)}, specifically comprising the steps of: The third subproblem is transformed into minimizing As the goal, Blog2(1+SINR k )≥R min To optimize the constraints {w k }The fourth subproblem, w k 、SINR k Represents w in a single time period k (t), SINR k(l) ; 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 the new constraint D i ≥ Re{} represents the real part operation. Indicates w i 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 9. A method for minimizing energy consumption of multi-antenna UAV communication according to claim 8, characterized in that: The fifth sub-problem is solved using a convex optimization problem solving tool.

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

Images