CN111541473B - Array antenna unmanned aerial vehicle base station-oriented track planning and power distribution method - Google Patents

Abstract

The invention discloses a flight path planning and power distribution method for an array antenna unmanned aerial vehicle base station, which is suitable for scenes such as unmanned aerial vehicle emergency communication and the like. The method comprises the following steps: establishing a channel fading model by utilizing an air-ground wireless channel model of the array antenna according to the geographical positions of the unmanned aerial vehicle and the user; establishing a user reachable rate model of the array antenna unmanned aerial vehicle base station communication system based on a precoding technology adopted by an array antenna transmitting end; based on a user reachable rate model, under the condition that the flight state and the transmitting power of the unmanned aerial vehicle are limited, a joint optimization problem of flight path planning and user power distribution is established by taking the maximum system transmission rate as a target; and transforming and solving the optimization problem by using a block coordinate rotation descent and continuous convex approximation method to obtain an unmanned aerial vehicle path planning and power distribution scheme based on the precoding technology. The method has important guiding significance for the deployment of the array antenna unmanned aerial vehicle base station communication system.

Description

A track planning and power allocation method for array antenna UAV base station

technical field

The invention belongs to the technical field of wireless communication, and in particular relates to a track planning and power distribution method for an array antenna unmanned aerial vehicle base station.

Background technique

Drone technology has developed rapidly in recent years. Due to the high flexibility and high controllability of UAVs, UAV-assisted wireless communication systems have broad application prospects, especially for various emergency communication needs. For example, in large-scale sudden disasters, or large-scale events, ground communication facilities may not be able to provide communication guarantees. At this time, the use of drones as air base stations can quickly and flexibly establish a wireless communication network to ensure information transmission.

At present, there are a lot of researches on the topic of UAV-assisted wireless communication at home and abroad, but the existing research mainly considers single-antenna UAVs, and rarely involves array antenna technology. Very few studies on array antenna UAVs only involve the location deployment of UAVs without considering its trajectory planning. Considering that UAVs provide communication services during flight, it is necessary to study UAV trajectory planning. As a high-altitude base station, since the total energy carried by the UAV is fixed, the optimization of power resources is also particularly important. Therefore, the planning of such UAV trajectories and communication resources is of great significance.

SUMMARY OF THE INVENTION

Purpose of the invention: Aiming at the gaps in the prior art, the present invention proposes a track planning and power allocation method for the array antenna UAV base station, which provides highly reliable technical guidance for the deployment of the array antenna UAV base station communication system.

Technical solution: a trajectory planning and power allocation method for an array antenna UAV base station, wherein the UAV base station is loaded with a set of array antennas to realize spatial diversity multiplexing, and respectively adopts Maximum Ratio Transmission (MRT) and Zero Forcing (ZF) precoding technology to precode the transmitted symbols, the trajectory planning and power allocation method can adjust the trajectory and power of the UAV under the condition of limited flight status and flight time. Allocation to maximize the system transmission rate, so as to obtain the optimal track and power allocation scheme, which includes the following steps:

(1) According to the geographic location of the UAV and ground users, the channel fading model is established by using the air-ground wireless channel model of the array antenna;

(2) Based on the precoding technology adopted by the array antenna transmitter, establish the user-reachable rate model of the array antenna UAV base station communication system;

(3) Based on the user reachable rate model, under the condition of UAV flight status and limited transmit power, with the goal of maximizing the system transmission rate, a joint optimization problem of trajectory planning and user power allocation is established;

(4) Transform and solve the above optimization problem by using the block coordinate rotation descent and continuous convex approximation method, and obtain the UAV path planning and power allocation scheme based on the precoding technology.

Wherein, when the precoding technology adopted by the array antenna transmitter is the MRT precoding technology, the step 2 establishes a user-reachable rate model of the array antenna UAV base station communication system based on the MRT precoding technology, and in the step 3 Establish the joint optimization problem of trajectory planning and power allocation based on MRT precoding technology (1); when the precoding technology adopted at the transmitting end of the array antenna is the ZF precoding technology, the step 2 establishes an array antenna based on the ZF precoding technology The user-reachable rate model of the UAV base station communication system, in the step 3, the joint optimization problem (2) of trajectory planning and power allocation based on ZF precoding technology is established.

Assuming that the flying height of the UAV is large enough, the channel between it and the user is the Line of Sight (LOS) channel; then in the time slot m, the channel fading model between the UAV and the user i is:

N是天线的个数，θ_i[m]表示阵列法向量和用户方向间的夹角；对于任意阵列，导向矢量表示为

τ_i为第i个阵元接收信号相对于原点信号的时延。Among them, d _i [m] is the distance between the UAV and the i-th user in the mth time slot; β ₀ represents the power gain when the distance between the UAV and the user is 1m; b(θ _i [m ]) represents the steering vector; when the array is a uniform linear array,

N is the number of antennas, θ _i [m] represents the angle between the array normal vector and the user direction; for any array, the steering vector is expressed as

τ _i is the time delay of the received signal of the i-th array element relative to the origin signal.

Based on the MRT precoding technology, the user-reachable rate model of the array antenna UAV base station communication system is established as follows:

表示用户i在时隙m的可达速率；N表示阵列天线中阵元的数量；p_i[m]表示无人机在时隙m给用户i分配的功率大小，I表示用户总数；σ²是地面用户的接收噪声功率。in,

Represents the reachable rate of user i in time slot m; N represents the number of array elements in the array antenna; p _i [m] represents the power allocated by UAV to user i in time slot m, I represents the total number of users; σ ² is the received noise power of the terrestrial user.

Based on the above user-reachable rate model, under the condition of UAV flight status and limited transmit power, with the goal of maximizing the system transmission rate, the joint optimization problem (1) of trajectory planning and power allocation is established as follows:

q ₀ =q[0],q _F =q[M], (1.d)

“max”表示最大化运算；“s.t.”表示约束条件；M表示总时隙数；(1.b)和(1.c)表示每个时隙的发射功率约束，P表示无人机最大发射功率；(1.d)和(1.e)是无人机飞行状态约束，q₀和q_F分别表示无人机的起始和终点位置，q[m]表示无人机在第m个时隙的位置，δ表示时隙大小；V_max表示无人机最大飞行速度。in

"max" represents the maximization operation; "st" represents the constraints; M represents the total number of time slots; (1.b) and (1.c) represent the transmit power constraints of each time slot, and P represents the maximum UAV transmission Power; (1.d) and (1.e) are the UAV flight state constraints, q ₀ and q _F represent the starting and ending positions of the UAV, respectively, and q[m] indicates that the UAV is in the mth The position of the time slot, δ represents the size of the time slot; V _max represents the maximum flight speed of the UAV.

Based on the ZF precoding technology, the user-reachable rate model of the array antenna UAV base station communication system is established as follows:

where γ _i [m]=1/[(H _i [m]H ^H [m]) ^-1 ] _ii , H[m]=[b ^T (θ _i [m]),...,b ^T (θ _I [m])] ^T , where I is the total number of users, and σ ² is the received noise power of terrestrial users.

Based on the above user-reachable rate model, under the condition of UAV flight status and limited transmit power, with the goal of maximizing the system transmission rate, the joint optimization problem (2) of trajectory planning and power allocation is established as follows:

q ₀ =q[0], q _F =q[M], (2.d)

“max”表示最大化运算；“s.t.”表示约束条件；M表示总时隙数；P表示无人机最大发射功率，(2.b)和(2.c)是发射功率约束；(2.d)和(2.e)表示无人机飞行状态约束，q₀和q_F分别表示无人机的起始和终点位置，q[m]表示无人机在第m个时隙的位置，δ表示时隙大小；V_max表示无人机最大飞行速度。in,

"max" represents the maximization operation; "st" represents the constraint condition; M represents the total number of time slots; P represents the maximum transmit power of the UAV, (2.b) and (2.c) are the transmit power constraints; (2. d) and (2.e) represent the flight state constraints of the UAV, q ₀ and q _F represent the start and end positions of the UAV, respectively, q[m] represents the position of the UAV in the mth time slot, δ represents the time slot size; V _max represents the maximum flight speed of the UAV.

Beneficial effects: The present invention proposes for the first time a method for track planning and power allocation for an array antenna UAV base station, establishing a user-reachable rate model according to the precoding technology adopted by the antenna transmitter, and is affected by the UAV flight state and flight time. Under the limited conditions, the system transmission rate is optimized by adjusting its trajectory and user power allocation. The invention can provide highly reliable technical guidance for the deployment of the array antenna unmanned aerial vehicle base station communication system.

Description of drawings

1 is a schematic diagram of a wireless communication system based on an array antenna UAV base station in the present invention;

Fig. 2 is the flow chart of the track planning and power distribution method for the array antenna UAV base station of the present invention;

Fig. 3 is the variation trend diagram of the system optimum rate with the flying height of the UAV in the present invention;

Fig. 4 is the variation trend diagram of λ corresponding to ZF precoding with time slot when the flying height of the drone is H=100m and H=400m in the present invention;

FIG. 5 is a change trend diagram of the system and speed with the number of UAV antennas, flight time and maximum transmit power in the present invention.

Detailed ways

The technical solutions of the present invention will be further described below with reference to the accompanying drawings.

As shown in Figure 1, this example provides a schematic diagram of a wireless communication system based on an array antenna UAV base station. In this communication system, consider a communication system with a single UAV as an aerial base station. In the process of flying from the starting position to the predetermined end position, it provides downlink transmission services for multiple users on the ground. The UAV base station is equipped with a set of array antennas to realize spatial diversity multiplexing, and uses the maximum ratio transmission (MRT) and zero forcing (ZF) precoding techniques to precode the transmitted symbols respectively. For the convenience of description, it is assumed that the UAV flies from the starting point to the end point in a straight line, that is, the direction of the array antenna is always the same as the flight direction. User power allocation to maximize system transmission rate. Referring to Figure 2, the specific steps are as follows:

Step 1, according to the geographic location of the UAV and the user and the air-to-ground wireless channel model based on the array antenna, establish the channel fading model:

表示在第m个时隙无人机与第i个用户之间距离；无人机在第m个时隙的水平坐标是q[m]＝[x[m],0]^T，高度为H；用户i的高度是零，水平坐标是

β₀表示无人机与用户间距离为1m时的功率增益；b(θ_i[m])表示导向矢量；当阵列为均匀线阵时，

N是天线的个数，θ_i[m]表示阵列法向量和用户方向间的夹角；对于任意阵列，导向矢量表示为

τ_i为第i个阵元接收信号相对于原点信号的时延。in,

Represents the distance between the UAV and the i-th user in the m-th time slot; the horizontal coordinate of the UAV in the m-th time slot is q[m]=[x[m],0] ^T , and the height is H ; the height of user i is zero and the horizontal coordinate is

β ₀ represents the power gain when the distance between the UAV and the user is 1m; b(θ _i [m]) represents the steering vector; when the array is a uniform linear array,

N is the number of antennas, θ _i [m] represents the angle between the array normal vector and the user direction; for any array, the steering vector is expressed as

τ _i is the time delay of the received signal of the i-th array element relative to the origin signal.

Assuming that the channel information is known, the signal received by the i-th user in the m-th time slot can be expressed as:

表示在第m个时隙所发送的预编码信号。where ( ) ^H represents the conjugate transpose operation, n _i is Gaussian white noise with zero mean and variance σ ² ,

represents the precoded signal transmitted in the mth slot.

In step 2, based on the MRT precoding technology, in the mth time slot, the transmitted signal s[m] is specifically expressed as:

和p_i[m]分别表示在第m个时隙，阵列天线相对于用户i的预编码矢量和发射功率，s_i[m]是用户数据且满足|s_i[m]|＝1。将式(3)代入表达式(2)，得到：in,

and p _i [m] respectively represent the precoding vector and transmit power of the array antenna relative to user i in the mth time slot, s _i [m] is user data and satisfies |s _i [m]|=1. Substituting equation (3) into expression (2), we get:

因此，在第m个时隙，用户i的接收信噪比(Signal to Interference plus Noise，SINR)可以表示为：Among them, a _ik [m]=b ^H (θ _i [m])b(θ _k [m]), i≠k,

Therefore, in the mth time slot, the received signal-to-noise ratio (Signal to Interference plus Noise, SINR) of user i can be expressed as:

Therefore, the user-reachable rate model of the array antenna UAV base station communication system based on MRT precoding technology is:

In step 3, the joint optimization problem of trajectory planning and power based on MRT precoding technology is as follows:

q ₀ =q[0],q _F =q[M], (7.d)

“max”表示最大化运算；“s.t.”表示约束条件；I表示用户总数；M表示总时隙数；(7.b)和(7.c)表示每个时隙的发射功率约束，P表示无人机最大发射功率；(7.d)和(7.e)是无人机飞行状态约束，q₀和q_F分别表示无人机的起始和终点位置，q[m]表示无人机在第m个时隙的位置，δ表示时隙大小；V_max表示无人机最大飞行速度。in,

"max" represents the maximization operation; "st" represents the constraint condition; I represents the total number of users; M represents the total number of time slots; (7.b) and (7.c) represent the transmit power constraints of each time slot, and P represents the The maximum launch power of the UAV; (7.d) and (7.e) are the flight state constraints of the UAV, q ₀ and q _F represent the starting and ending positions of the UAV, respectively, and q[m] represents the unmanned The position of the drone in the mth time slot, δ represents the size of the time slot; V _max represents the maximum flight speed of the drone.

In step 4, for the optimization problem (7) based on MRT precoding in step 3, the solution process is as follows:

To solve this problem, the block coordinate rotation descent method is used to decompose the original problem into two sub-problems to solve.

1) Fixed track optimization power: when the track is given, the objective problem (7) is simplified to the power distribution sub-problem:

s.t.(7.b),(7.c), (8.b)

的全局凹下界，然后利用迭代方法不断最大化该下界，从而优化原问题。该优化思想被称为连续凸近似(successive convexapproximation,SCA)[Razaviyayn M.:‘Successive convex approximation:analysisand applications’,Ph.D.dissertation,University of Minnesota,2014]。下面推导函数

的凹下界，由式子(6)可得According to the user-reachable rate definitions in equations (5) and (6), the objective function in problem (8) is still non-convex with respect to the power variable. Therefore, the present invention first derives

The global concave lower bound of , and then use an iterative method to continuously maximize the lower bound to optimize the original problem. This optimization idea is called continuous convex approximation (SCA) [Razaviyayn M.:'Successive convex approximation: analysis and applications', Ph.D. dissertation, University of Minnesota, 2014]. Derive the function below

The concave lower bound of , can be obtained from equation (6)

和

均是关于变量发射功率的凹函数。根据定义，凹函数在任意点的一阶泰勒展开都是其全局上限。根据这一特性，

在发射功率

处的全局上界为：in,

and

are concave functions with respect to the variable transmit power. By definition, the first-order Taylor expansion of a concave function at any point is its global upper limit. According to this characteristic,

at transmit power

The global upper bound at is:

对于任意给定的

有

问题(8)的SCA迭代子问题为以下凸优化问题：where μ is the number of iterations. because

for any given

Have

The SCA iterative subproblem of problem (8) is the following convex optimization problem:

s.t.(7.b),(7.c), (11.b)

并作为第(μ+1)次迭代的初始值。当前后两次迭代的目标函数之差的绝对值小于

时，终止迭代，并输出所得发射功率。The above problem is solved by optimization algorithms such as interior point method, and the optimal solution is expressed as

And as the initial value of the (μ+1)th iteration. The absolute value of the difference between the objective functions of the current and last two iterations is less than

, terminate the iteration, and output the resulting transmit power.

2) Fixed-power optimized track: Given the UAV’s transmit power allocation for each time slot, the following track planning sub-problem is obtained:

s.t.(7.d), (7.e), (12.b)

是变量q的非凸函数，所以问题(12)是非凸的。为了解决该问题，本发明采用SCA思想，首先推导目标函数的全局凹下界，然后利用迭代方法不断最大化该下界，从而优化原问题。在

中，|a_ik[m]|²是q[m]的复杂函数，导致问题难以优化。因此，在|a_ik[m]|²中利用上一轮迭代输出的

来近似q[m]，从而将|a_ik[m]|²变为常数，以简化问题。为了使得

成立，引入正则项

其中α是一个可调参数。当α不断增加时，问题(12)中的最优q[m]将不断接近

在第ε次迭代中，令

并在Q^ε[m]处对

进行一阶泰勒展开，得到其凹下界：in,

is a nonconvex function of variable q, so problem (12) is nonconvex. In order to solve this problem, the present invention adopts the idea of SCA, firstly deriving the global concave lower bound of the objective function, and then using the iterative method to continuously maximize the lower bound, thereby optimizing the original problem. exist

, |a _ik [m]| ² is a complex function of q[m], which makes the problem difficult to optimize. Therefore, in |a _ik [m]| ² , the output of the previous iteration is used to

to approximate q[m], thus making |a _ik [m]| ² constant to simplify the problem. in order to make

is established, the regular term is introduced

where α is a tunable parameter. As α keeps increasing, the optimal q[m] in problem (12) will keep getting closer

In the εth iteration, let

and at Q ^ε [m] for

Perform first-order Taylor expansion to get its concave lower bound:

in,

E _i [m]=β ₀ N ² p _i [m], (14)

This leads to the SCA iteration subproblem of problem (12):

作为下一次迭代的初始点。当迭代收敛时，输出无人机航迹规划。st(7.d), (7.e), (16.b) The above problem (16) is a convex problem, which can be quickly solved by optimization methods such as interior point method, and the optimal solution is expressed as

as the starting point for the next iteration. When the iteration converges, the UAV trajectory plan is output.

The solution algorithm of trajectory planning and power joint optimization problem (7) based on MRT precoding technology is summarized as follows:

Algorithm A:

迭代次数k＝0；1: Initialize the UAV track q ^k [m] and power according to the constraints in the problem

The number of iterations k = 0;

2: Fix the UAV track, and iteratively optimize the transmit power by using a continuous convex approximation until convergence;

3: Fix the power distribution between users obtained in 2, and use the continuous convex approximation algorithm to iteratively optimize the UAV track,

until convergence;

4: Execute 1 and 2 iteratively based on the block coordinate rotation descent method until convergence.

In step 5, when ZF precoding is used, there is no interference between users. According to equation (2), in the mth time slot, the received signal of user i is:

in,

P是无人机的最大发射功率，

是用户i的预编码矢量。定义信道状态矩阵为

则采用ZF预编码时，Among them, p _i [m] represents the transmit power of the UAV to user i in the time slot m, and satisfies the

P is the maximum transmit power of the drone,

is the precoding vector for user i. Define the channel state matrix as

Then when ZF precoding is used,

是矩阵

的第i列，in,

is the matrix

the i-th column of ,

V[m]=H ^H [m](H[m]H ^H [m])- ¹ (20)

Therefore, the user-reachable rate model of the array antenna UAV base station communication system based on ZF precoding technology is:

Wherein, λ _i [m]=τ _i [m]γ _i [m]/σ ² , and γ _i [m]=1/[(H _i [m]H ^H [m]) ⁻¹ ] _ii .

In step 6, the joint optimization problem of trajectory planning and power allocation based on ZF precoding technology is as follows:

q ₀ =q[0],q _F =q[M], (22.d)

Among them, (22.b) and (22.c) are the transmit power constraints; (22.d) and (22.e) represent the UAV flight state constraints.

In step 7, since the optimization problem (22) in step 6 is non-convex, the present invention adopts the idea of block coordinate rotation descent and continuous convex approximation to solve this problem, and the specific process is as follows:

Based on the idea of block coordinate rotation descent, firstly, the UAV track q[m] is given to update the power p _i [m]; then, the track is updated given the user's power allocation. The specific process is as follows:

1) Fixed track optimization power: When the UAV track is given, problem (22) can be written as the following sub-problems:

s.t.(22.b),(22.c), (23.b)

This problem can be solved by a convex optimization method, such as the interior point method. For details, please refer to [S.P.Boyd and L.Vandenberghe, Convex Optimization.Cambridge, U.K.: Cambridge Univ.Press, 2004] to obtain the optimal power distribution.

2) Fixed power optimization path: When the power allocation is given, the UAV’s path planning sub-problem is as follows:

s.t.(22.d), (22.e), (24.b)

是q[m]的非凸函数，

为正则项，其作用与问题(18)中相同。in,

is a non-convex function of q[m],

is a regular term, and its function is the same as in problem (18).

的凹下界，将式(21)展开可得Problem (24) is non-convex, and it is solved here through the SCA idea, that is, it is continuously maximized through an iterative method

The concave lower bound of , expand Eq. (21) to get

是一个常量。因此，对于任意给定的

有

的凹下界：When the power is given,

is a constant. Therefore, for any given

Have

The concave lower bound of :

in,

Therefore, the SCA iteration subproblem of problem (24) is

s.t.(22.d), (22.e), (29.b)

作为下次迭代的初始值。当SCA迭代收敛时，输出得到的最优功率分配。Problem (29) is a convex optimization problem, which can be solved by optimization methods such as interior point method, and its optimal solution can be expressed as

as the initial value for the next iteration. When the SCA iteration converges, the resulting optimal power distribution is output.

The algorithm for solving the trajectory planning and power allocation optimization problem (22) based on ZF precoding is summarized as follows:

Algorithm B:

1: According to the constraints in the problem, initialize the trajectory q ^k [m] of the UAV, and the number of iterations k = 0;

2: Fix the UAV track, and use the interior point method to optimize the power distribution among users;

3: Fix the power distribution between users obtained in 2, and use continuous convex approximation to iteratively optimize the UAV track until the algorithm converges;

4: Rotate descent iterations based on block coordinates to perform 2 and 3 until convergence.

It should be understood that the above method presented in the description of steps 1 to 7 is only for the purpose of clearly and completely explaining the technical solutions of the present invention, and does not limit the method to be performed in the order of steps 1 to 7. Some of these may be performed, or certain steps may be performed in parallel, or in the reverse order, without departing from the spirit of the invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Based on the detailed step description of the above-mentioned power allocation method for the trajectory planning of the array antenna UAV base station, the performance of the method is verified by a simulation example below. In this example, Matlab software is used to simulate the system, and CVX software package is used to solve the optimization problem. Throughout the simulation, the UAV always flies in a straight line and provides data transmission services for ground users. The following simulations have no special instructions, and the relevant parameters are set as follows: UAV flight height H=150m; start position and end position are [0,0,H] and [1500,0,H] respectively; maximum flight speed V _max = 40m/s; the number of elements of the linear array antenna is N=4; the horizontal coordinates of the three ground users are w ₁ =[300,0], w ₂ =[800,50] and w ₃ =[1200,0 respectively ]; the total transmit power of the UAV is P=0.1W; the noise power is σ ² =-110dBm; the channel power gain relative to the reference distance β ₀ =-50dBm; the total flight time T=50s, assuming that the length of each time slot is δ=0.1s, so the total number of time slots is M=500.

Figure 3 is a trend diagram of the optimal system and rate with the flight height of the UAV. It can be seen from the figure that in the system based on ZF precoding, the system sum rate first increases and then decreases with the increase of height. where the system and rate increase with height is counter-intuitive. Because the height of the drone is relatively small and it is close to the user, the user's reachable rate is relatively large; when the drone's height increases, it is far away from the user, and the reachable rate should gradually decrease. However, when the height is too small, the difference between the incident angles of the short-distance user (U1) and the far-distance user (U2) is large, at this time |b ^H (θ ₁ )b(θ ₂ )|≈0; at the same time, in order to Serving the short-range user U1, the corresponding steering vector x has a strong correlation with b ^H (θ ₁ ), that is, x≈b ^H (θ ₁ ), so |xb(θ ₂ )|≈0, which causes the long-distance user’s λ to be very high. small (as shown in Figure 4), that is, the achievable rate is very small. So when the altitude is too low, the overall velocity drops. When the height exceeds a certain value, the signal fading is larger, which in turn leads to a decrease in the signal-to-noise ratio and a decrease in the system and rate. In the MRT-based precoding system, the system sum rate decreases gradually with the increase of height, the reason is that the increase of the distance between the UAV and the user leads to the increase of signal attenuation, so that the system sum rate becomes smaller.

Figure 5 shows the relationship between the system and rate and the flight time of the UAV, the number of antennas, and the total transmit power in the MRT and ZF-based precoding systems, respectively. When the total time is constant, with the increase of the number of antennas, the system sum rate will also increase; when the number of antennas is constant, with the increase of the total time, the system sum rate will also increase. In addition, when the number of antennas, total transmit power, and total time are the same, the system and rate obtained by using ZF precoding are larger than those obtained by MRT precoding. This is because in this system, the main factor affecting system performance during interference is ZF precoding. Coding eliminates inter-user interference. The invention has important guiding significance for the deployment of the array antenna unmanned aerial vehicle base station communication system.

Claims (3)

1. A flight path planning and power distribution method facing an array antenna unmanned aerial vehicle base station is characterized by comprising the following steps:

(1) according to the geographical positions of the unmanned aerial vehicle and the ground user, an air-ground wireless channel model of the array antenna is utilized to establish a channel fading model as follows:

wherein d is_i[m]Is the distance between the drone and the ith user in the mth time slot; beta is a₀Representing the power gain when the distance between the unmanned aerial vehicle and the user is 1 m; b (theta)_i[m]) Representing a steering vector; when the array is a uniform linear array,

n is the number of antennas, θ_i[m]Representing the included angle between the array normal vector and the user direction; for an arbitrary array, the steering vector is represented as

τ_iThe time delay of the signal received by the ith array element relative to the original point signal;

(2) based on a precoding technology adopted by an array antenna transmitting end, a user reachable rate model of an array antenna unmanned aerial vehicle base station communication system is established, and the method specifically comprises the following steps:

when the precoding technology adopted by the array antenna transmitting end is MRT precoding technology, a user reachable rate model of the array antenna unmanned aerial vehicle base station communication system based on the MRT precoding technology is established as follows:

wherein,

representing the achievable rate of user i in time slot m; p is a radical of_i[m]The power distributed to the user I by the unmanned aerial vehicle in the time slot m is represented, and I represents the total number of the users; sigma²Is the received noise power of the user;

when the precoding technology adopted by the array antenna transmitting end is ZF precoding technology, establishing a user reachable rate model of the array antenna unmanned aerial vehicle base station communication system based on the ZF precoding technology as follows:

wherein, γ_i[m]＝1/[(H_i[m]H^H[m])^-1]_ii，H[m]＝[b^T(θ_i[m]),...,b^T(θ_I[m])]^T；

(3) Based on a user reachable rate model, under the condition that the flight state and the transmitting power of the unmanned aerial vehicle are limited, a joint optimization problem of flight path planning and user power distribution is established by taking the maximum system transmission rate as a target, wherein,

the flight path planning and power joint optimization problem based on the MRT precoding technology (1) is as follows:

q₀＝q[0],q_F＝q[M], (1.d)

wherein

max represents a maximization operation; s.t. represents a constraint; m represents the total time slot number; (1.b) and (1.c) represent the transmit power constraint per time slot, P represents the drone maximum transmit power; (1.d) and (1.e) are unmanned aerial vehicle flight state constraints, q₀And q is_FRespectively representing the starting and ending positions of the drone, qm]The position of the unmanned plane in the mth time slot is shown, and delta represents the size of the time slot; v_maxRepresenting the maximum flight speed of the unmanned aerial vehicle;

the flight path planning and power distribution joint optimization problem (2) based on the ZF precoding technology is as follows:

q₀＝q[0],q_F＝q[M], (2.d)

wherein,

max represents a maximization operation; s.t. represents a constraint; m represents the total time slot number; p represents the drone maximum transmit power, (2.b) and (2.c) are transmit power constraints; (2.d) and (2.e) represent unmanned aerial vehicle flight state constraints, q₀And q is_FRespectively representing the starting and ending positions of the drone, qm]The position of the unmanned plane in the mth time slot is shown, and delta represents the size of the time slot; v_maxRepresenting the maximum flight speed of the unmanned aerial vehicle;

(4) and transforming and solving the optimization problem by using a block coordinate rotation descent and continuous convex approximation method to obtain an unmanned aerial vehicle path planning and power distribution scheme based on the precoding technology.

2. The array antenna-oriented unmanned aerial vehicle base station track planning and power distribution method according to claim 1, wherein in the step 4, the problem (1) is converted and solved by using a block coordinate rotation descent and continuous convex approximation method, and the specific steps are as follows:

step 1), initializing the flight path of the unmanned aerial vehicle according to the constraint conditions in the problem (1);

step 2), fixing the flight path of the unmanned aerial vehicle, and optimizing the transmitting power by using continuous convex approximation until the algorithm is converged;

step 3), fixing power distribution among users, and iteratively optimizing the flight path of the unmanned aerial vehicle by using a continuous convex approximation algorithm until the algorithm is converged;

and 4) iteratively executing the step 2) and the step 3) based on the block coordinate rotation descent method until convergence.

3. The array antenna-oriented unmanned aerial vehicle base station track planning and power distribution method according to claim 1, wherein in the step 4, the problem (2) is converted and solved by using a block coordinate rotation descent and continuous convex approximation method, and the specific steps are as follows:

step 1), initializing the flight path of the unmanned aerial vehicle according to the constraint conditions in the problem (2);

step 2), fixing the flight path of the unmanned aerial vehicle, and optimizing power distribution among users by using an interior point method;

step 3), fixing power distribution among users, and optimizing the flight path of the unmanned aerial vehicle by utilizing continuous convex approximation iteration until the algorithm is converged;

and 4) iterating the step 2) and the step 3) based on the block coordinate rotation descent method until convergence.

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