A method for jointly allocating UAV trajectories and resources and computer-readable medium

Abstract

The present invention proposes a joint allocation method for UAV trajectories and resources and a computer-readable medium. The present invention calculates the maximum transmission rate of the UAV to each ground user at any horizontal position; constructs a UAV optimization solution target; then inputs the UAV's initial iterative trajectory point set, initial iterative hovering time set, and the UAV's scheduling coefficient set for all users within the hovering point and flight segment; uses a convex approximation method to construct a convex optimization solution target for each iteration of the UAV; obtains the optimized solution for each iteration by iteratively solving the convex optimization solution target obtained by the ellipsoid method; and repeats the above solution steps until the change value of the auxiliary variable is less than the iteration threshold, then outputs the optimal solution. The present invention has the characteristics of low complexity, can achieve low occupancy of computing resources, is suitable for large-scale cluster communication networks such as future UAV cellular networking, and has extremely high economic benefits.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) wireless communications, and in particular relates to a method for jointly allocating UAV trajectories and resources and a computer-readable medium. Background Art

[0002] Among the current auxiliary communication technologies for 5G wireless networks, drones, due to their rapid deployment and high controllability, have made the use of drones carrying communication base stations to build aerial mobile networks a promising approach to addressing the high infrastructure and operating costs of 5G networks. In particular, drone-supported wireless communication networks are becoming a hot topic of research due to the high probability of high-quality line-of-sight (LOS) channels within their air-to-ground (A2G) channels. By integrating drone-supported wireless communication systems into terrestrial cellular networks, network coverage and throughput performance can be significantly improved. Furthermore, using drones as relays to provide stronger multi-hop wireless links between remote ground users can achieve wireless coverage for even distant users.

[0003] At the same time, due to the significant advantages of drones in mobility, the performance of wireless communication networks supported by drones can be significantly improved by optimizing the trajectory of drones, and related research work is also in full swing. In 2019, X.Zhou et al. proposed a covert communication solution to solve the security problem of drone wireless communication systems. By rationally planning the transmission power and trajectory of drones, the system throughput is maximized while ensuring transmission concealment. In 2022, S.Zhang et al. considered the cellular network system supported by drones and minimized the task completion time by optimizing the trajectory of drones. In the same year, G.Zhang et al. maximized the system throughput by rationally planning the transmission power and trajectory of drones for wireless communication systems supported by drones.

[0004] While a series of studies on trajectory design in UAV wireless communication systems, such as the aforementioned work, have shown some promise in improving system performance, most of these approaches rely on time-division quantization to optimize UAV trajectories. This makes the algorithms extremely complex, limiting their practical application. Furthermore, most current work assumes a Loss of Sight (LOS) channel between the UAV and ground users, which is impractical in densely populated urban environments. A more general Ricean channel model should be employed. Therefore, further research is needed to develop low-complexity optimal UAV trajectory design methods for Ricean channels. Summary of the Invention

[0005] Faced with the wireless communication scenarios supported by drones in 5G networks, and the current lack of efficient drone trajectory design methods under general channels, the present invention proposes a drone trajectory and resource joint allocation method and computer-readable medium to improve the system performance of drone-supported wireless networks and reduce the complexity of system design.

[0006] The technical solution of the method of the present invention is a method for jointly allocating UAV trajectories and resources, comprising the following steps:

[0007] Step 1: Select a UAV and multiple ground users, introduce the position of each ground user, combine the UAV height, channel gain per unit distance, UAV transmit power, ambient noise power, and Ricean channel transmission rate approximation coefficient, and calculate the maximum transmission rate of the UAV to each ground user at any horizontal position;

[0008] Step 2: Introduce a continuous hovering flight structure to construct the set of UAV flight trajectory points, the set of UAV scheduling coefficients for all ground users at the hovering points, the set of UAV scheduling coefficients for all ground users in the flight segment, and the set of UAV hovering time. Calculate the flight trajectory length between UAV trajectory points, build a position model for the UAV between trajectory points, construct data transmission targets, data transmission constraints, mission time constraints, hovering time constraints, and ground user scheduling coefficient constraints, and further construct the UAV optimization solution target.

[0009] Step 3: Input the initial iterative trajectory point set of the UAV, the initial iterative hovering time set of the UAV, and the initial scheduling coefficient set of the UAV for all ground users within the hovering point and flight segment;

[0010] Step 4: In each iteration, a convex approximation method is used to obtain the lower bound concave approximation function of the UAV's throughput to ground users at the hovering point and the lower bound concave approximation function of the UAV's throughput to ground users during the flight segment; a data transmission target is constructed for each iteration; based on the concave approximation function of the UAV's throughput to ground users at the hovering point and the lower bound concave approximation function of the UAV's throughput to ground users during the flight segment, a convex data transmission constraint is constructed for each iteration, and a convex optimization solution target for the UAV is further constructed for each iteration;

[0011] Step 5: Obtain the convex optimization solution target of each iteration of the UAV through step 4, and obtain the optimal solution of each iteration through the ellipsoid method;

[0012] Step 6: Repeat step 5 until the change in the auxiliary variable is less than the iteration threshold, then output the optimal set of drone trajectory points, the optimal set of hovering times, the optimal set of scheduling coefficients for drones at the hovering points to all ground users, and the optimal set of scheduling coefficients for drones within the flight segment to all ground users.

[0013] Preferably, the number of ground users in step 1 is K;

[0014] The location of each ground user in step 1 is defined as follows:

[0015] w k =(w x,k ,w y,k ),k=1,...,K

[0016] Among them, k is the number of the ground user, w k represents the location of the kth ground user, w x,k is the x coordinate of the kth ground user, w y,k is the y coordinate of the kth ground user.

[0017] The altitude of the drone described in step 1 is defined as H;

[0018] The channel gain per unit distance described in step 1 is defined as β0;

[0019] The UAV transmission power described in step 1 is defined as P;

[0020] The ambient noise power mentioned in step 1 is defined as σ 2 ;

[0021] The first approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as B1;

[0022] The second approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as B2;

[0023] The third approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as C1;

[0024] The fourth approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as C2;

[0025] Step 1 calculates the maximum transmission rate of the drone to each ground user at any horizontal position as follows:

[0026]

[0027]

[0028] Among them, k represents the number of the ground user, q represents the arbitrary horizontal position of the UAV, and R k(q) represents the maximum transmission rate of the UAV to the kth ground user at any position q; B1 represents the first approximate coefficient of the Ricean channel transmission rate, B2 represents the second approximate coefficient of the Ricean channel transmission rate, C1 represents the third approximate coefficient of the Ricean channel transmission rate, and C2 represents the fourth approximate coefficient of the Ricean channel transmission rate; H represents the flight altitude of the UAV, β0 represents the channel gain per unit distance, P represents the UAV transmission power, σ 2 represents the ambient noise power; d k (q) represents the distance from the UAV at any location q to the kth ground user, w k Represents the location of the kth ground user.

[0029] Preferably, the trajectory point set of the flight described in step 2 is defined as:

[0030] Q=(q 0,0 ,q 0,1 ,...,q 0,N ,...,q i,0 ,q i,1 ,...,q i,j ,q i,N ,...,q K,N ,q K+1,0 ),i=1,...,K,j=1,...,N

[0031] Where i, j represent the number of the drone trajectory point, K represents the number of ground users, and N represents the number of turning points between two hovering points; q i,j represents the coordinates of the jth trajectory point after the i-th hovering point; q 0,0 represents the starting coordinates of the drone, q K+1,0 represents the coordinates of the drone's endpoint, q 0,j Represents the coordinates of the jth trajectory point after the starting point of the drone.

[0032] The set of scheduling coefficients of the drone at the hovering point for the ground user described in step 2 is defined as:

[0033] A hov,k =[a 1,k ,...,a i,k ,...,a K,k ],i=1,...,K

[0034] Among them, k represents the number of the ground user, i represents the number of the hovering point, and K represents the number of ground users; A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, a i,k represents the scheduling coefficient of the UAV at the i-th hovering point to the k-th ground user.

[0035] The set of scheduling coefficients of the drone at the hovering point for all ground users in step 2 is defined as:

[0036] A hov =[A hov,1 ,...,A hov,k ,...,A hov,K ],k=1,...,K

[0037] Among them, k represents the number of ground users, K represents the number of ground users; A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A hov,k Represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user.

[0038] The scheduling coefficient set of the UAV for ground users in the flight segment described in step 2 is defined as:

[0039] A fly,k =[a 0,0,k ,...,a 0,N,k ,a 1,0,k ,...,a i,j,k ,...,a K,N,k ],i=0,...,K,j=0,...,N

[0040] Where k represents the number of the ground user, i, j represents the trajectory point number, K represents the number of ground users, and N represents the number of turning points between two hovering points; A fly,k represents the set of scheduling coefficients of the UAV for the kth ground user in the flight segment, a i,j,k Represents the jth trajectory point q of the drone after it hovers from the i-th hovering point i,j The j+1th trajectory point q after the i-th hovering point i,j The scheduling coefficient for the kth ground user in the flight segment.

[0041] The scheduling coefficient set of the UAV for all ground users in the flight segment described in step 2 is defined as:

[0042] A fly =[A fly,1 ,...,A fly,k ,...,A fly,K ],k=1,...,K

[0043] Among them, k represents the number of ground users, K represents the number of ground users; A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, A fly,k Represents the set of scheduling coefficients of the UAV for the kth ground user within the flight segment.

[0044] The hovering time set of the UAV described in step 2 is defined as:

[0045] Λ=(λ1,...,λ i ,...,λ K ),i=1,...,K

[0046] Where K represents the number of ground users, i represents the hovering point number, Λ represents the hovering time set of the human-machine, and λ i The hovering time of the drone at the i-th hovering point.

[0047] The flight trajectory length between the UAV trajectory points in step 2 is defined as:

[0048] d i,j (Q)=||q i,j+1 -q i,j ||,i=0,...,K,j=0,...,N

[0049] Where i, j represent the trajectory point numbers, K represents the number of ground users, and N represents the number of turning points between two hovering points; q i,j represents the coordinates of the jth trajectory point after the i-th hovering point, q i,j+1 represents the coordinates of the j+1th trajectory point after the i-th hovering point; d i,j (Q) represents the UAV's trajectory from point q i,j To trajectory point q i,j+1 Length of flight trajectory.

[0050] The position model of the drone between the trajectory points in step 2 is:

[0051]

[0052] Where i, j represent the trajectory point numbers, K represents the number of ground users, and N represents the number of turning points between two hovering points; q i,j represents the coordinates of the jth trajectory point after the i-th hovering point, q i,j+1 represents the coordinates of the j+1th trajectory point after the i-th hovering point, t i,j Represents the drone leaving the trajectory point q i,j is the time when the timing starts, q i,j (t i,j ) represents the UAV from the trajectory point q i,j To trajectory point q i,j+1 Flight time i,j The position at the moment; V represents the maximum flight speed of the drone.

[0053] The throughput of UAV to ground users during the mission time described in step 2 is defined as:

[0054]

[0055] Among them, k represents the number of the ground user, i, j represents the trajectory point number, K represents the number of ground users, N represents the number of turning points between two hovering points; Q represents the set of flight trajectory points of the UAV, A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, Λ represents the set of hovering time of the UAV; q represents the horizontal position of any UAV, R k (q) represents the maximum transmission rate of the UAV to the kth ground user at any location q; Ψ k (Q,Λ,A hov ,A fly ) represents the throughput of the UAV to the kth ground user during the entire mission time; q i represents the position of the i-th hovering point of the drone, λ i Represents the drone at the i-th hovering point q i Hover time, t i,j Represents the drone leaving the trajectory point q i,j is the time when the timing starts, q i,j (t i,j ) represents the jth trajectory point q after the drone starts from the i-th hovering point i,j The j+1th trajectory point q after the i-th hovering point i,j+1 Flight time i,j Position at the moment; d k (q i ) represents the drone in q i The distance to the kth ground user, d k (q i,j (t i,j )) represents the drone at position q i,j (t i,j ) to the kth ground user; d i,j (Q) represents the UAV's trajectory from point q i,j To trajectory point q i,j+1 The length of the flight trajectory; a i,k represents the scheduling coefficient of the UAV at the i-th hovering point to the k-th ground user; a i,j,k Represents the drone moving from trajectory point q i,j To trajectory point q i,j+1Scheduling coefficient for the kth ground user during flight; B1 represents the first approximate coefficient of the Ricean channel transmission rate, B2 represents the second approximate coefficient of the Ricean channel transmission rate, C1 represents the third approximate coefficient of the Ricean channel transmission rate, and C2 represents the fourth approximate coefficient of the Ricean channel transmission rate; H represents the flight altitude of the UAV, β0 represents the channel gain per unit distance, P represents the UAV transmission power, σ 2 represents the ambient noise power, and V represents the maximum flight speed of the drone.

[0056] The total mission time of the UAV described in step 2 is defined as:

[0057]

[0058] Where i, j represent the trajectory point numbers, K represents the number of ground users, N represents the number of turning points between two hovering points; Q represents the set of flight trajectory points of the UAV, Λ represents the set of hovering time of the UAV; Ω(Q,Λ) represents the total mission time of the UAV; λ i is the hovering time of the UAV at the i-th hovering point; d i,j (Q) represents the UAV's trajectory from point q i,j To trajectory point q i,j+1 The length of the flight trajectory; V represents the maximum flight speed of the UAV.

[0059] The data transmission target in step 2 is:

[0060]

[0061] Among them, D is the auxiliary variable introduced, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; max means maximization.

[0062] The data transfer constraints described in step 2 are defined as:

[0063] Ψ k (Q,Λ,A hov ,A fly )≥D,k=1,...,K

[0064] Among them, k represents the number of ground users, K represents the number of ground users; Q represents the set of flight trajectory points of the UAV, A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, Λ represents the set of hovering time of the UAV; k(Q,Λ,A hov ,A fly ) represents the throughput of the UAV to the kth ground user during the total mission time, and D represents the defined auxiliary variable.

[0065] The task time constraint described in step 2 is defined as:

[0066] Ω(Q,Λ)≤T

[0067] Among them, Q represents the set of flight trajectory points of the UAV, Λ represents the set of hovering time of the UAV; Ω(Q,Λ) represents the total mission time of the UAV, and T represents the given maximum mission time.

[0068] The hover time constraint described in step 2 is defined as:

[0069] Λ≥0

[0070] Where Λ represents the set of drone hovering times.

[0071] The ground user scheduling coefficient constraint described in step 2 is defined as:

[0072]

[0073]

[0074] Where k represents the number of ground users, K represents the number of ground users; A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, A fly,k Represents the set of scheduling coefficients of the UAV for the kth ground user within the flight segment.

[0075] The optimization solution goal of the UAV in step 2 is:

[0076]

[0077] Ψ k (Q,Λ,A hov ,A fly )≥D,k=1,...,K

[0078] Ω(Q,Λ)≤T

[0079] Λ≥0

[0080]

[0081]

[0082] Among them, k represents the number of ground users, K represents the number of ground users; D represents the auxiliary variable introduced, Q represents the set of UAV trajectory points, Λ represents the set of UAV hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, Ψ k (Q,Λ,A hov ,A fly ) represents the throughput of the UAV to the kth ground user during the mission time, Ω(Q,Λ) represents the total mission time of the UAV, T represents the given maximum mission time, A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, A fly,k Represents the set of scheduling coefficients of the UAV for the kth ground user in the flight segment, and max represents maximization.

[0083] Preferably, the initial iterative trajectory point set of the UAV in step 3 is defined as:

[0084]

[0085] Where i, j represent the trajectory point numbers, K represents the number of ground users, N represents the number of turning points between two hovering points; Q (0) Represents the set of initial iterative trajectory points of the drone; Represents the coordinates of the jth trajectory point after the i-th hovering point in the initial iteration.

[0086] The initial iterative hovering time set of the UAV in step 3 is

[0087] Λ (0) =0

[0088] Among them, Λ (0) Represents the initial hover time set.

[0089] The initial scheduling coefficient set of the drone at the hovering point for all ground users in step 3 is

[0090]

[0091] Where K represents the number of ground users, represents the initial scheduling set of the hovering point UAV for all ground users; the initial scheduling coefficient set of the UAV for all ground users in the flight segment described in step 3 is:

[0092]

[0093] Where K represents the number of ground users, represents the initial dispatch set of the hovering point UAV to all ground users;

[0094] Preferably, in each iteration of step 4, a concave approximation function of the lower bound of the throughput of the drone to the ground user at the hovering point is obtained by a convex approximation method:

[0095]

[0096] Among them, k represents the number of the ground user, i represents the hovering point number, r represents the iteration number; Q represents the set of flight trajectory points of the UAV, Λ represents the set of hovering time of the UAV, and A hov represents the set of scheduling coefficients of the UAV at the hovering point for all ground users; q i represents the i-th hovering point of the drone, λ i Represents the drone at the i-th hovering point q i The hovering time at a i,k Represents the drone at the i-th hovering point q i Scheduling coefficient for the kth ground user; d k (q i ) represents the drone at the i-th hovering point q i The distance to k ground users; Represents the rth iteration by using the convex approximation method to obtain the drone's hovering point q at the i-th point i Concave approximation function for the lower bound of the k-th terrestrial user throughput; Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The first approximation coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The second approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The third approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The fourth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t iConcave approximation function for the lower bound of the kth terrestrial user throughput The fifth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The sixth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The seventh approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The eighth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The ninth approximate coefficient of .

[0097] In each iteration of step 4, the concave approximate function of the lower bound of the UAV’s throughput to ground users in the flight segment is obtained by the convex approximation method:

[0098]

[0099] Among them, k represents the ground user number, i,j represents the trajectory point number, r represents the iteration number; Q represents the set of UAV trajectory points, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; q i,j represents the jth trajectory point after the i-th hovering point of the drone, q i,j+1 represents the j+1th trajectory point after the i-th hovering point of the drone; a i,j,k Represents the drone from trajectory point q i,j To trajectory point q i,j+1 Scheduling coefficient for the kth ground user in the flight segment; τ represents the time auxiliary variable introduced, q i ' ,j (τ) represents the distance from the UAV to the trajectory point q i,j To trajectory point q i,j+1 The position of the τ variable in the flight segment; d k (q i ' ,j (τ)) represents the position of the UAV at q i ' ,j(τ) The distance to the kth ground user, d i,j (Q) represents the UAV trajectory point q i,j To trajectory point q i,j+1 The length of the flight path, Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The flight trajectory length d i,j (Q) is the lower bound approximation function; Represents the rth iteration by convex approximation method to obtain the UAV from the trajectory point q i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the k-th ground user throughput in the flight segment; Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The first approximation coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The second approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The third approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The fourth approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The fifth approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The sixth approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The seventh approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The eighth approximate coefficient; V represents the maximum flight speed of the drone.

[0100] Step 4 defines the data transfer target for each iteration as follows:

[0101]

[0102] Among them, r represents the iteration number; D (r) represents the auxiliary variable introduced in the rth iteration, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; max means maximization.

[0103] The convex data transmission constraint for each iteration described in step 4 is defined as:

[0104]

[0105] Where k represents the number of the ground user, i, j represents the number of trajectory points, K represents the number of ground users, N represents the number of turning points between two hovering points, and r represents the number of iterations; D (r) represents the auxiliary variable introduced in the rth iteration, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; Represents the rth iteration by using the convex approximation method to obtain the drone's hovering point q at the i-th point i Concave approximation function for the lower bound of the k-th terrestrial user throughput; Represents the jth trajectory point q obtained by the convex approximation method from the i-th hovering point in the r-th iteration i,j The j+1th trajectory point q after the i-th hovering point i,j+1 The concave approximate function of the lower bound of the k-th ground user throughput in the flight segment.

[0106] The convex optimization solution target of each iteration of the drone described in step 4 is:

[0107]

[0108]

[0109] Ω(Q,Λ)≤T

[0110] Λ≥0

[0111]

[0112]

[0113] Where k represents the number of the ground user, i, j represents the number of trajectory points, K represents the number of ground users, N represents the number of turning points between two hovering points, and r represents the number of iterations; D (r) represents the auxiliary variable introduced in the rth iteration, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; Represents the rth iteration by using the convex approximation method to obtain the drone's hovering point q at the i-th point i Concave approximation function for the lower bound of the k-th terrestrial user throughput; Represents the jth trajectory point q of the drone after the i-th hovering point obtained by the convex approximation method in the r-th iteration i,j The j+1th trajectory point q after the i-th hovering point i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment; Ω(Q,Λ) represents the total mission time of the UAV, T represents the given maximum mission time; A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, A fly,k represents the set of scheduling coefficients of the UAV for the kth ground user in the flight segment; max represents maximization;

[0114] Preferably, the optimized solution of each iteration in step 5 is:

[0115]

[0116] Among them, Q (r) represents the optimal solution of the set of UAV trajectory points at the rth iteration, Λ (r) represents the optimal solution of the drone hovering time set for the rth iteration, represents the optimal solution of the scheduling coefficient set of the hovering point UAV for all ground users in the rth iteration, represents the optimal solution of the scheduling coefficient set of the UAV to all ground users in the flight segment of the rth iteration;

[0117] Preferably, the change in the auxiliary variable in step 6 is less than the iteration threshold, and the specific judgment process is as follows:

[0118] D (r) -D (r-1) <ε

[0119] Among them, D (r) The auxiliary variable representing the rth iteration is obtained by step 4, D (r-1) The auxiliary variable representing the r-1th iteration is calculated in step 4, and ε represents the iteration threshold;

[0120] The optimal set of drone trajectory points in step 6 is Q * ;

[0121] The optimal hovering time set in step 6 is Λ * ;

[0122] The optimal scheduling coefficient set of the drone at the hovering point for all ground users in step 6 is

[0123] The optimal scheduling coefficient set of the UAV for all ground users in the flight segment described in step 6 is

[0124] Output the optimal set of drone trajectory points, hovering time set, the set of scheduling coefficients of drones for all ground users at the hovering point, and the set of scheduling coefficients of drones for all ground users in the flight segment

[0125] The present invention also provides a computer-readable medium, which stores a computer program executed by an electronic device. When the computer program runs on the electronic device, it performs the steps of the method for jointly allocating drone trajectories and resources.

[0126] This paper proposes a low-complexity joint design algorithm for drone trajectory and resource scheduling under Ricean channels. It can achieve efficient trajectory planning and resource scheduling calculations in drone-supported communication systems under constraints such as mission time and the maximum flight speed of the drone. While significantly improving the throughput of the communication network, this optimization algorithm can achieve low occupancy of computing resources, thereby freeing up excess computing power to perform tasks in parallel. It has extremely high economic benefits and great scalability, and can be applied to large-scale cluster communication networks such as future drone cellular networks.

[0127] Contents of attached figure

[0128] Figure 1 : Schematic diagram of a UAV communication system scenario according to an embodiment of the present invention;

[0129] Figure 2 : Flow chart of the method implemented by the present invention. Specific implementation plan

[0130] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0131] In specific implementation, the method proposed in the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. System devices that implement the method, such as computer-readable storage media that store the corresponding computer program of the technical solution of the present invention and computer equipment that runs the corresponding computer program, should also be within the scope of protection of the present invention.

[0132] The following combination Figure 1-2 The technical solution of the method of the embodiment of the present invention is a method for jointly allocating UAV trajectories and resources, which is specifically as follows:

[0133] Figure 1 The present invention considers a UAV communication system under a Rice channel, which includes a UAV and K ground users. The UAV is moving from the starting point q0 = (0, 0) to the end point q F =(1000,1000) sends data to ground users during flight. The UAV's flight altitude is fixed at H, the maximum flight speed is V=10, the given maximum mission time is T=300, and the position of the ground user is known. Let the kth, k=1,...,Kth ground user position be w k =(w x,k ,w y,k ). The channel gain per unit distance is β0, the approximate coefficients of the transmission rate under the Ricean channel are B1, B2, C1, C2, the transmission power of the drone is P, and the noise power is σ 2 To improve the performance of wireless networks supported by drones, it is necessary to jointly design the drone's trajectory and the scheduling coefficients for ground users to maximize the system throughput within a given maximum mission time T. This joint design of trajectory and ground user scheduling involves solving a very large number of drone position variables in continuous time and solving the ground user scheduling problem, making the solution very complex.

[0134] like Figure 2 Shown is a flow chart of the method of the present invention.

[0135] Step 1: Select a UAV and multiple ground users, introduce the location and environmental channel information of each ground user, and combine the UAV altitude, channel gain per unit distance, UAV transmit power, environmental noise power, and Ricean channel transmission rate approximation coefficient to calculate the maximum transmission rate of the UAV to each ground user at any horizontal position;

[0136] The number of ground users in step 1 is K=5;

[0137] The location of each ground user in step 1 is defined as follows:

[0138] w k =(w x,k ,w y,k ),k=1,...,K

[0139] Where k is the number of the ground user, K=5 represents the number of ground users; w k represents the location of the kth ground user, w x,k is the x coordinate of the kth ground user, w y,k is the y coordinate of the kth ground user.

[0140] The altitude of the drone described in step 1 is defined as H = 30;

[0141] The channel gain per unit distance in step 1 is defined as β0 = -30 dBm;

[0142] The UAV transmission power described in step 1 is defined as P = 0.1;

[0143] The ambient noise power mentioned in step 1 is defined as σ 2 =-80dBm;

[0144] The first approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as B1=-0.52;

[0145] The second approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as B2=5.3;

[0146] The third approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as C1=0.011;

[0147] The fourth approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as C2=0.92;

[0148] Step 1 calculates the maximum transmission rate of the drone to each ground user at any horizontal position as follows:

[0149]

[0150]

[0151] Among them, k represents the number of the ground user, q represents the arbitrary horizontal position of the UAV, and R k (q) represents the maximum transmission rate of the UAV to the kth ground user at any position q; B1 = -0.52 represents the first approximate coefficient of the Ricean channel transmission rate, B2 = 5.3 represents the second approximate coefficient of the Ricean channel transmission rate, C1 = 0.011 represents the third approximate coefficient of the Ricean channel transmission rate, and C2 = 0.92 represents the fourth approximate coefficient of the Ricean channel transmission rate; H = 30 represents the flight altitude of the UAV, β0 = -30dBm represents the channel gain at unit distance, P = 0.1 represents the UAV transmission power, σ 2 =-80dBm represents the ambient noise power; d k (q) represents the distance from the UAV at any location q to the kth ground user, w k Represents the location of the kth ground user.

[0152] Step 2: Introduce the continuous hovering flight structure and define the number of turning points N between two hovering points. Construct the set of UAV flight trajectory points, the set of UAV scheduling coefficients for each ground user at the hovering point, the set of UAV scheduling coefficients for all ground users at the hovering point, the set of UAV scheduling coefficients for all ground users in the flight segment, and the set of UAV hovering time. Further calculate the flight trajectory length between UAV trajectory points, build the UAV position model between trajectory points, construct the data transmission target, data transmission constraints, mission time constraints, hovering time constraints, and ground user scheduling coefficient constraints, and further construct the UAV optimization solution target.

[0153] The number of turning points between the two hovering points in step 2 is N=2.

[0154] The set of trajectory points for the flight described in step 2 is defined as:

[0155] Q=(q 0,0 ,q 0,1 ,...,q 0,N ,...,q i,0 ,q i,1 ,...,q i,j ,q i,N ,...,q K,N ,q K+1,0 ),i=1,...,K,j=1,...,N

[0156] Where i, j represent the number of the drone trajectory points, K = 5 represents the number of ground users, N = 2 represents the number of turning points between two hovering points; q i,j represents the coordinates of the jth trajectory point after the i-th hovering point; q 0,0=q0=(0,0) represents the starting coordinates of the drone, q K+1,0 =q F =(1000,1000) represents the coordinates of the drone’s endpoint, q 0,j Represents the coordinates of the jth trajectory point after the starting point of the drone.

[0157] The set of scheduling coefficients of the drone at the hovering point for the ground user described in step 2 is defined as:

[0158] A hov,k =[a 1,k ,...,a i,k ,...,a K,k ],i=1,...,K

[0159] Where k represents the number of the ground user, i represents the number of the hovering point, and K=5 represents the number of ground users; A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, a i,k represents the scheduling coefficient of the UAV at the i-th hovering point to the k-th ground user.

[0160] The set of scheduling coefficients of the drone at the hovering point for all ground users in step 2 is defined as:

[0161] A hov =[A hov,1 ,...,A hov,k ,...,A hov,K ],k=1,...,K

[0162] Where k represents the number of the ground user, K=5 represents the number of ground users; A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A hov,k Represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user.

[0163] The scheduling coefficient set of the UAV for ground users in the flight segment described in step 2 is defined as:

[0164] A fly,k =[a 0,0,k ,...,a 0,N,k ,a 1,0,k ,...,a i,j,k ,...,a K,N,k ],i=0,...,K,j=0,...,N

[0165] Where k represents the number of the ground user, i, j represents the number of the trajectory points, K = 5 represents the number of ground users, N = 2 represents the number of turning points between two hovering points; A fly,krepresents the set of scheduling coefficients of the UAV for the kth ground user in the flight segment, a i,j,k Represents the jth trajectory point q of the drone after it hovers from the i-th hovering point i,j The j+1th trajectory point q after the i-th hovering point i,j The scheduling coefficient for the kth ground user in the flight segment.

[0166] The scheduling coefficient set of the UAV for all ground users in the flight segment described in step 2 is defined as:

[0167] A fly =[A fly,1 ,...,A fly,k ,...,A fly,K ],k=1,...,K

[0168] Where k represents the number of the ground user, K=5 represents the number of ground users; A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, A fly,k Represents the set of scheduling coefficients of the UAV for the kth ground user within the flight segment.

[0169] The hovering time set of the UAV described in step 2 is defined as:

[0170] Λ=(λ1,...,λ i ,...,λ K ),i=1,...,K

[0171] Where K = 5 represents the number of ground users, i represents the hovering point number, Λ represents the hovering time set of the human-machine, and λ i The hovering time of the UAV at the i-th hovering point.

[0172] The flight trajectory length between the UAV trajectory points in step 2 is defined as:

[0173] d i,j (Q)=||q i,j+1 -q i,j ||,i=0,...,K,j=0,...,N

[0174] Where i, j represent the trajectory point numbers, K = 5 represents the number of ground users, N = 2 represents the number of turning points between two hovering points; q i,j represents the coordinates of the jth trajectory point after the i-th hovering point, q i,j+1 represents the coordinates of the j+1th trajectory point after the i-th hovering point; d i,j (Q) represents the UAV's trajectory from point q i,j To trajectory point q i,j+1 Length of flight trajectory.

[0175] The position model of the drone between the trajectory points in step 2 is:

[0176]

[0177] Where i, j represent the trajectory point numbers, K = 5 represents the number of ground users, N = 2 represents the number of turning points between two hovering points; q i,j represents the coordinates of the jth trajectory point after the i-th hovering point, q i,j+1 represents the coordinates of the j+1th trajectory point after the i-th hovering point, t i,j Represents the drone leaving the trajectory point q i,j is the starting time of the timing, q i,j (t i,j ) represents the UAV from the trajectory point q i,j To trajectory point q i,j+1 Flight time i,j The position at the moment; V = 10 represents the maximum flight speed of the drone.

[0178] The throughput of UAV to ground users during the mission time described in step 2 is defined as:

[0179]

[0180] Among them, k represents the number of ground users, i, j represents the number of trajectory points, K = 5 represents the number of ground users, N = 2 represents the number of turning points between two hovering points; Q represents the set of flight trajectory points of the UAV, A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, Λ represents the set of hovering time of the UAV; q represents the horizontal position of any UAV, R k (q) represents the maximum transmission rate of the UAV to the kth ground user at any location q; Ψ k (Q,Λ,A hov ,A fly ) represents the throughput of the UAV to the kth ground user during the entire mission time; q i represents the position of the i-th hovering point of the drone, λ i Represents the drone at the i-th hovering point q i Hover time, t i,j Represents the drone leaving the trajectory point q i,j is the time when the timing starts, q i,j (t i,j ) represents the jth trajectory point q after the drone starts from the i-th hovering point i,j The j+1th trajectory point q after the i-th hovering point i,j+1 Flight timei,j Position at the moment; d k (q i ) represents the drone in q i The distance to the kth ground user, d k (q i,j (t i,j )) represents the drone at position q i,j (t i,j ) to the kth ground user; d i,j (Q) represents the UAV's trajectory from point q i,j To trajectory point q i,j+1 The length of the flight trajectory; a i,k represents the scheduling coefficient of the UAV at the i-th hovering point to the k-th ground user; a i,j,k Represents the drone moving from trajectory point q i,j To trajectory point q i,j+1 Scheduling coefficient for the kth ground user during flight; B1 = -0.52 represents the first approximate coefficient of the Ricean channel transmission rate, B2 = 5.3 represents the second approximate coefficient of the Ricean channel transmission rate, C1 = 0.011 represents the third approximate coefficient of the Ricean channel transmission rate, and C2 = 0.92 represents the fourth approximate coefficient of the Ricean channel transmission rate; H = 30 represents the flight altitude of the UAV, β0 = -30dBm represents the channel gain per unit distance, P = 0.1 represents the UAV transmission power, σ 2 =-80dBm represents the ambient noise power, and V=10 represents the maximum flight speed of the drone.

[0181] The total mission time of the UAV described in step 2 is defined as:

[0182]

[0183] Where i, j represent the trajectory point numbers, K = 5 represents the number of ground users, N = 2 represents the number of turning points between two hovering points; Q represents the set of flight trajectory points of the UAV, Λ represents the set of hovering time of the UAV; Ω(Q,Λ) represents the total mission time of the UAV; λ i is the hovering time of the UAV at the i-th hovering point; d i,j (Q) represents the UAV's trajectory from point q i,j To trajectory point q i,j+1 The length of the flight trajectory; V = 10 represents the maximum flight speed of the UAV.

[0184] The data transmission target in step 2 is:

[0185]

[0186] Among them, D is the defined auxiliary variable, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; max means maximization.

[0187] The data transfer constraints described in step 2 are defined as:

[0188] Ψ k (Q,Λ,A hov ,A fly )≥D,k=1,...,K

[0189] Among them, k represents the number of ground users, K = 5 represents the number of ground users; Q represents the set of flight trajectory points of the UAV, A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, Λ represents the set of hovering time of the UAV; k (Q,Λ,A hov ,A fly ) represents the throughput of the UAV to the kth ground user during the total mission time, and D represents the defined auxiliary variable.

[0190] The task time constraint described in step 2 is defined as:

[0191] Ω(Q,Λ)≤T

[0192] Among them, Q represents the set of flight trajectory points of the UAV, Λ represents the set of hovering time of the UAV; Ω(Q,Λ) represents the total mission time of the UAV, and T=300 represents the given maximum mission time.

[0193] The hover time constraint described in step 2 is defined as:

[0194] Λ≥0

[0195] Where Λ represents the set of drone hovering times.

[0196] The ground user scheduling coefficient constraint described in step 2 is defined as:

[0197]

[0198]

[0199] Wherein, k represents the number of the ground user, K=5 represents the number of ground users; A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, Afly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, A fly,k Represents the set of scheduling coefficients of the UAV for the kth ground user within the flight segment.

[0200] The optimization solution goal of the UAV described in step 2 is:

[0201]

[0202] Ψ k (Q,Λ,A hov ,A fly )≥D,k=1,...,K

[0203] Ω(Q,Λ)≤T

[0204] Λ≥0

[0205]

[0206]

[0207] Where k represents the number of ground users, K = 5 represents the number of ground users; D represents the defined auxiliary variable, Q represents the set of UAV trajectory points, Λ represents the set of UAV hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, Ψ k (Q,Λ,A hov ,A fly ) represents the throughput of the UAV to the kth ground user during the mission time, Ω(Q,Λ) represents the total mission time of the UAV, T represents the given maximum mission time, A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, A fly,k represents the set of scheduling coefficients of the UAV for the kth ground user in the flight segment, and max represents maximization;

[0208] Step 3: Input the initial iterative trajectory point set of the UAV, the initial iterative hovering time set of the UAV, and the initial scheduling coefficient set of the UAV for all ground users within the hovering point and flight segment;

[0209] The initial iterative trajectory point set of the UAV described in step 3 is defined as:

[0210]

[0211] Where i, j represent the trajectory point numbers, K = 5 represents the number of ground users, N = 2 represents the number of turning points between two hovering points; Q (0) Represents the set of initial iterative trajectory points of the drone; Represents the coordinates of the jth trajectory point after the i-th hovering point in the initial iteration.

[0212] The initial iterative hovering time set of the UAV in step 3 is

[0213] Λ (0) =0

[0214] Among them, Λ (0) Represents the initial hover time set.

[0215] The initial scheduling coefficient set of the drone at the hovering point for all ground users in step 3 is

[0216]

[0217] Where K represents the number of ground users, represents the initial dispatch set of the hovering point UAV to all ground users;

[0218] The initial scheduling coefficient set of the UAV for all ground users in the flight segment described in step 3 is:

[0219]

[0220] Where K represents the number of ground users, represents the initial dispatch set of the hovering point UAV to all ground users;

[0221] Step 4: In each iteration, the convex approximation method is used to obtain the lower bound concave approximation function of the UAV's throughput to ground users at the hovering point and the lower bound concave approximation function of the UAV's throughput to ground users during the flight segment; construct the data transmission target for each iteration; based on the concave approximation function of the lower bound concave approximation function of the UAV's throughput to ground users at the hovering point and the lower bound concave approximation function of the UAV's throughput to ground users during the flight segment, construct the convex data transmission constraint for each iteration, and further construct the convex optimization solution target for the UAV in each iteration.

[0222] In each iteration of step 4, the concave approximation function of the lower bound of the throughput of the drone to the ground user at the hovering point is obtained by the convex approximation method:

[0223]

[0224] Among them, k represents the number of the ground user, i represents the hovering point number, r represents the iteration number; Q represents the set of flight trajectory points of the UAV, Λ represents the hovering time set of the UAV, and A hovrepresents the set of scheduling coefficients of the UAV at the hovering point for all ground users; q i represents the i-th hovering point of the drone, λ i Represents the drone at the i-th hovering point q i The hovering time at a i,k Represents the drone at the i-th hovering point q i Scheduling coefficient for the kth ground user; d k (q i ) represents the drone at the i-th hovering point q i The distance to k ground users; Represents the rth iteration by using the convex approximation method to obtain the drone's hovering point q at the i-th point i Concave approximation function for the lower bound of the k-th terrestrial user throughput; Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The first approximation coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The second approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The third approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The fourth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The fifth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The sixth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The seventh approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The eighth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The ninth approximate coefficient of .

[0225] In each iteration of step 4, the concave approximate function of the lower bound of the UAV’s throughput to ground users in the flight segment is obtained by the convex approximation method:

[0226]

[0227] Among them, k represents the ground user number, i,j represents the trajectory point number, r represents the iteration number; Q represents the set of UAV trajectory points, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; q i,j represents the jth trajectory point after the i-th hovering point of the drone, q i,j+1 represents the j+1th trajectory point after the i-th hovering point of the UAV; a i,j,k Represents the drone from trajectory point q i,j To trajectory point q i,j+1 Scheduling coefficient for the kth ground user in the flight segment; τ represents the time auxiliary variable introduced, q i ' ,j (τ) represents the distance from the UAV to the trajectory point q i,j To trajectory point q i,j+1 The position of the τ variable in the flight segment; d k (q i ' ,j (τ)) represents the position of the UAV at q i ' ,j (τ) The distance to the kth ground user, d i,j (Q) represents the UAV trajectory point q i,j To trajectory point q i,j+1 The length of the flight trajectory, Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The flight trajectory length d i,j (Q) is the lower bound approximation function; Represents the rth iteration by using the convex approximation method to obtain the UAV from the trajectory point q i,j To trajectory point q i,j+1The concave approximate function of the lower bound of the k-th ground user throughput in the flight segment; Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The first approximation coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The second approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The third approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The fourth approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The fifth approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The sixth approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The seventh approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The eighth approximate coefficient; V = 10 represents the maximum flight speed of the drone.

[0228] Step 4 defines the data transfer target for each iteration as follows:

[0229]

[0230] Among them, r represents the iteration number; D (r) represents the auxiliary variable introduced in the rth iteration, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; max means maximization.

[0231] The convex data transmission constraint for each iteration described in step 4 is defined as:

[0232]

[0233] Where k represents the number of the ground user, i, j represents the number of trajectory points, K = 5 represents the number of ground users, N = 2 represents the number of turning points between two hovering points, and r represents the number of iterations; D (r) represents the auxiliary variable introduced in the rth iteration, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; Represents the rth iteration by using the convex approximation method to obtain the drone's hovering point q at the i-th point i Concave approximation function for the lower bound of the k-th terrestrial user throughput; Represents the jth trajectory point q obtained by the convex approximation method from the i-th hovering point in the r-th iteration i,j The j+1th trajectory point q after the i-th hovering point i,j+1 The concave approximate function of the lower bound of the k-th ground user throughput in the flight segment.

[0234] The convex optimization solution target of each iteration of the drone described in step 4 is:

[0235]

[0236]

[0237] Ω(Q,Λ)≤T

[0238] Λ≥0

[0239]

[0240]

[0241] Where k represents the number of the ground user, i, j represents the number of trajectory points, K = 5 represents the number of ground users, N = 2 represents the number of turning points between two hovering points, and r represents the number of iterations; D (r) represents the auxiliary variable introduced in the rth iteration, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; Represents the rth iteration by using the convex approximation method to obtain the drone's hovering point q at the i-th point i Concave approximation function for the lower bound of the k-th terrestrial user throughput; Represents the jth trajectory point q of the drone after the i-th hovering point obtained by the convex approximation method in the r-th iteration i,j The j+1th trajectory point q after the i-th hovering point i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment; Ω(Q,Λ) represents the total mission time of the UAV, T represents the given maximum mission time; A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, A fly,k Represents the set of scheduling coefficients of the UAV for the kth ground user during the flight segment; max represents maximization.

[0242] Step 5: Obtain the convex optimization solution target of each iteration of the UAV through step 4, and obtain the optimal solution of each iteration through the ellipsoid method;

[0243] The optimized solution for each iteration in step 5 is:

[0244]

[0245] Among them, Q (r) represents the optimal solution of the set of UAV trajectory points at the rth iteration, Λ (r) represents the optimal solution of the drone hovering time set for the rth iteration, represents the optimal solution of the scheduling coefficient set of the hovering point UAV for all ground users in the rth iteration, represents the optimal solution of the scheduling coefficient set of the UAV to all ground users in the flight segment of the rth iteration;

[0246] Step 6: Repeat step 5 until the change in the auxiliary variable is less than the iteration threshold, then output the optimal set of drone trajectory points, the optimal set of hovering times, the optimal set of scheduling coefficients for drones at the hovering points to all ground users, and the optimal set of scheduling coefficients for drones within the flight segment to all ground users.

[0247] The change in the auxiliary variable in step 6 is less than the iteration threshold. The specific judgment process is as follows:

[0248] D (r) -D (r-1) <ε

[0249] Among them, D (r) The auxiliary variable representing the rth iteration is obtained by step 4, D (r-1) The auxiliary variable representing the r-1th iteration is calculated in step 4, and ε represents the iteration threshold;

[0250] The optimal set of drone trajectory points in step 6 is Q * ;

[0251] The optimal hovering time set in step 6 is Λ * ;

[0252] The optimal scheduling coefficient set of the drone at the hovering point for all ground users in step 6 is

[0253] The optimal scheduling coefficient set of the UAV for all ground users in the flight segment described in step 6 is

[0254] Output the optimal set of drone trajectory points, hovering time set, the set of scheduling coefficients of drones for all ground users at the hovering point, and the set of scheduling coefficients of drones for all ground users in the flight segment

[0255] A specific embodiment of the present invention also provides a computer-readable medium.

[0256] The computer readable medium is a server workstation;

[0257] The server workstation stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method for jointly allocating drone trajectories and resources in an embodiment of the present invention.

[0258] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0259] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of protection of the patent of the present invention. Under the guidance of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested by the present invention shall be based on the attached claims.

Claims

1. A method for joint allocation of drone trajectories and resources, characterized in that: The following steps are involved: Step 1: Select a UAV and multiple ground users, introduce the position of each ground user, combine the UAV height, channel gain per unit distance, UAV transmit power, ambient noise power, and Ricean channel transmission rate approximation coefficient, and calculate the maximum transmission rate of the UAV to each ground user at any horizontal position; Step 2: Construct a set of UAV flight trajectory points, a set of UAV scheduling coefficients for all ground users at hovering points, a set of UAV scheduling coefficients for all ground users in flight segments, and a set of UAV hovering times. Calculate the flight trajectory length between UAV trajectory points, construct a UAV position model between trajectory points, construct data transmission targets, data transmission constraints, mission time constraints, hovering time constraints, and ground user scheduling coefficient constraints, and further construct the UAV optimization solution target. Step 3: Input the initial iterative trajectory point set of the UAV, the initial iterative hovering time set of the UAV, and the initial scheduling coefficient set of the UAV for all ground users within the hovering point and flight segment; Step 4: In each iteration, a convex approximation method is used to obtain the lower bound concave approximation function of the UAV's throughput to ground users at the hovering point and the lower bound concave approximation function of the UAV's throughput to ground users during the flight segment. The data transmission target and convex data transmission constraints are constructed for each iteration, and the convex optimization solution target of the UAV is further constructed for each iteration. Step 5: Obtain the convex optimization solution target of each iteration of the UAV through step 4, and obtain the optimal solution of each iteration through the ellipsoid method; Step 6: Repeat step 5 until the change in the auxiliary variable is less than the iteration threshold, then output the optimal set of drone trajectory points, the optimal set of hovering times, the optimal set of scheduling coefficients for drones at the hovering points for all ground users, and the optimal set of scheduling coefficients for drones within the flight segment for all ground users.

2. The method for jointly allocating drone trajectories and resources according to claim 1, characterized in that: The number of ground users in step 1 is K; The position of each ground user in step 1 is defined as follows: w k =(w x,k ,w y,k ),k=1,...,K Among them, k is the number of the ground user, w k represents the location of the kth ground user, w x,k is the x coordinate of the kth ground user, w y,k is the y coordinate of the kth ground user; The altitude of the drone described in step 1 is defined as H; The channel gain per unit distance described in step 1 is defined as β0; The UAV transmission power described in step 1 is defined as P; The ambient noise power mentioned in step 1 is defined as σ 2 ; The first approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as B1; The second approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as B2; The third approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as C1; The fourth approximate coefficient of the Ricean channel transmission rate described in step 1 is defined as C2; Step 1 calculates the maximum transmission rate of the drone to each ground user at any horizontal position as follows: Among them, k represents the number of the ground user, q represents the arbitrary horizontal position of the UAV, and R k (q) represents the maximum transmission rate of the UAV to the kth ground user at any position q; B1 represents the first approximate coefficient of the Ricean channel transmission rate, B2 represents the second approximate coefficient of the Ricean channel transmission rate, C1 represents the third approximate coefficient of the Ricean channel transmission rate, and C2 represents the fourth approximate coefficient of the Ricean channel transmission rate; H represents the flight altitude of the UAV, β0 represents the channel gain per unit distance, P represents the UAV transmission power, σ 2 represents the ambient noise power; d k (q) represents the distance from the UAV at any location q to the kth ground user, w k Represents the location of the kth ground user.

3. The method for jointly allocating UAV trajectories and resources according to claim 2, characterized in that: The set of trajectory points for the flight described in step 2 is defined as: Q=(q 0,0 ,q 0,1 ,...,q 0,N ,...,q i,0 ,q i,1 ,...,q i,j ,q i,N ,...,q K,N ,q K+1,0 ),i=1,...,K,j=1,...,N Where i, j represent the number of the drone trajectory point, K represents the number of ground users, and N represents the number of turning points between two hovering points; q i,j represents the coordinates of the jth trajectory point after the i-th hovering point; q 0,0 Represents the starting coordinates of the drone, q K+1,0 represents the coordinates of the drone's endpoint, q 0,j Represents the coordinates of the jth trajectory point after the starting point of the UAV; The set of scheduling coefficients of the drone at the hovering point for the ground user described in step 2 is defined as: A hov,k =[a 1,k ,...,a i,k ,...,a K,k ],i=1,...,K Among them, k represents the number of the ground user, i represents the number of the hovering point, and K represents the number of ground users; A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, a i,k represents the scheduling coefficient of the UAV at the i-th hovering point to the k-th ground user; The set of scheduling coefficients of the drone at the hovering point for all ground users in step 2 is defined as: A hov =[A hov,1 ,...,A hov,k ,...,A hov,K ],k=1,...,K Among them, k represents the number of ground users, K represents the number of ground users; A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user; The scheduling coefficient set of the UAV for ground users in the flight segment described in step 2 is defined as: A fly,k =[a 0,0,k ,...,a 0,N,k ,a 1,0,k ,...,a i,j,k ,...,a K,N,k ],i=0,...,K,j=0,...,N Among them, k represents the number of the ground user, i, j represents the trajectory point number, K represents the number of ground users, and N represents the number of turning points between two hovering points; A fly,k represents the set of scheduling coefficients of the UAV for the kth ground user in the flight segment, a i,j,k Represents the jth trajectory point q of the drone after it hovers from the i-th hovering point i,j The j+1th trajectory point q after the i-th hovering point i,j The scheduling coefficient for the kth ground user in the flight segment; The scheduling coefficient set of the UAV for all ground users in the flight segment described in step 2 is defined as: A fly =[A fly,1 ,...,A fly,k ,...,A fly,K ],k=1,...,K Among them, k represents the number of ground users, K represents the number of ground users; A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, A fly,k represents the set of scheduling coefficients of the UAV for the kth ground user in the flight segment; The hovering time set of the UAV described in step 2 is defined as: Λ=(λ1,...,λ i ,...,l K ),i=1,...,K Where K represents the number of ground users, i represents the hovering point number, Λ represents the hovering time set of the human-machine, and λ i The hovering time of the UAV at the i-th hovering point.

4. The method for jointly allocating UAV trajectories and resources according to claim 3, characterized in that: The flight trajectory length between the UAV trajectory points in step 2 is defined as: d i,j (Q)=||q i,j+1 -q i,j ||,i=0,...,K,j=0,...,N Where i, j represent the trajectory point numbers, K represents the number of ground users, and N represents the number of turning points between two hovering points; q i,j represents the coordinates of the jth trajectory point after the i-th hovering point, q i,j+1 represents the coordinates of the j+1th trajectory point after the i-th hovering point; d i,j (Q) represents the UAV's trajectory from point q i,j To trajectory point q i,j+1 Length of flight trajectory; The position model of the drone between the trajectory points in step 2 is: Where i, j represent the trajectory point numbers, K represents the number of ground users, and N represents the number of turning points between two hovering points; q i,j represents the coordinates of the jth trajectory point after the i-th hovering point, q i,j+1 represents the coordinates of the j+1th trajectory point after the i-th hovering point, t i,j Represents the drone leaving the trajectory point q i,j is the time when the timing starts, q i,j (t i,j ) represents the UAV from the trajectory point q i,j To trajectory point q i,j+1 Flight time i,j The position at the moment; V represents the maximum flight speed of the drone; The throughput of UAV to ground users during the mission time described in step 2 is defined as: Among them, k represents the number of the ground user, i, j represents the trajectory point number, K represents the number of ground users, N represents the number of turning points between two hovering points; Q represents the set of flight trajectory points of the UAV, A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, Λ represents the set of hovering time of the UAV; q represents the horizontal position of any UAV, R k (q) represents the maximum transmission rate of the UAV to the kth ground user at any location q; Ψ k (Q,Λ,A hov ,A fly ) represents the throughput of the UAV to the kth ground user during the entire mission time; q i represents the position of the i-th hovering point of the drone, λ i Represents the drone at the i-th hovering point q i Hover time, t i,j Represents the drone leaving the trajectory point q i,j is the time when the timing starts, q i,j (t i,j ) represents the jth trajectory point q after the drone starts from the i-th hovering point i,j The j+1th trajectory point q after the i-th hovering point i,j+1 Flight time i,j Position at the moment; d k (q i ) represents the drone in q i The distance to the kth ground user, d k (q i,j (t i,j )) represents the drone at position q i,j (t i,j ) to the kth ground user; d i,j (Q) represents the UAV's trajectory from point q i,j To trajectory point q i,j+1 The length of the flight trajectory; a i,k represents the scheduling coefficient of the UAV at the i-th hovering point to the k-th ground user; a i,j,k Represents the drone moving from trajectory point q i,j To trajectory point q i,j+1 Scheduling coefficient for the kth ground user during flight; B1 represents the first approximate coefficient of the Ricean channel transmission rate, B2 represents the second approximate coefficient of the Ricean channel transmission rate, C1 represents the third approximate coefficient of the Ricean channel transmission rate, and C2 represents the fourth approximate coefficient of the Ricean channel transmission rate; H represents the flight altitude of the UAV, β0 represents the channel gain per unit distance, P represents the UAV transmission power, σ 2 represents the ambient noise power, and V represents the maximum flight speed of the drone; The total mission time of the UAV described in step 2 is defined as: Where i, j represent the trajectory point numbers, K represents the number of ground users, N represents the number of turning points between two hovering points; Q represents the set of flight trajectory points of the UAV, Λ represents the set of hovering time of the UAV; Ω(Q,Λ) represents the total mission time of the UAV; λ i is the hovering time of the UAV at the i-th hovering point; d i,j (Q) represents the UAV's trajectory from point q i,j To trajectory point q i,j+1 The length of the flight trajectory; V represents the maximum flight speed of the UAV.

5. The method for jointly allocating UAV trajectories and resources according to claim 4, characterized in that: The data transmission target in step 2 is: Among them, D is the auxiliary variable introduced, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; max means maximization; The data transfer constraints described in step 2 are defined as: P k (Q,Λ,A hov ,A fly )≥D,k=1,...,K Among them, k represents the number of ground users, K represents the number of ground users; Q represents the set of flight trajectory points of the UAV, A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, Λ represents the set of hovering time of the UAV; k (Q,Λ,A hov ,A fly ) represents the throughput of the UAV to the kth ground user during the total mission time, and D represents the defined auxiliary variable; The task time constraint described in step 2 is defined as: Ω(Q,Λ)≤T Where Q represents the set of flight trajectory points of the UAV, Λ represents the set of hovering time of the UAV; Ω(Q,Λ) represents the total mission time of the UAV, and T represents the given maximum mission time; The hover time constraint described in step 2 is defined as: Λ≥0 Where Λ represents the set of drone hovering time; The ground user scheduling coefficient constraint described in step 2 is defined as: Where k represents the number of ground users, K represents the number of ground users; A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, A fly,k represents the set of scheduling coefficients of the UAV for the kth ground user in the flight segment; The optimization solution goal of the UAV described in step 2 is: P k (Q,Λ,A hov ,A fly )≥D,k=1,...,K Ω(Q,Λ)≤T Λ≥0 Among them, k represents the number of ground users, K represents the number of ground users; D represents the auxiliary variable introduced, Q represents the set of UAV trajectory points, Λ represents the set of UAV hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment, Ψ k (Q,Λ,A hov ,A fly ) represents the throughput of the UAV to the kth ground user during the mission time, Ω(Q,Λ) represents the total mission time of the UAV, T represents the given maximum mission time, A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, A fly,k Represents the set of scheduling coefficients of the UAV for the kth ground user in the flight segment, and max represents maximization.

6. The method for jointly allocating UAV trajectories and resources according to claim 5, characterized in that: The initial iterative trajectory point set of the UAV described in step 3 is defined as: Where i, j represent the trajectory point numbers, K represents the number of ground users, N represents the number of turning points between two hovering points; Q (0) Represents the set of initial iterative trajectory points of the drone; represents the coordinates of the jth trajectory point after the i-th hovering point in the initial iteration; The initial iterative hovering time set of the UAV in step 3 is L (0) =0 Among them, Λ (0) Represents the initial hover time set; The initial scheduling coefficient set of the drone at the hovering point for all ground users in step 3 is: Where K represents the number of ground users, represents the initial dispatch set of the hovering point UAV to all ground users; The initial scheduling coefficient set of the UAV for all ground users in the flight segment described in step 3 is: Where K represents the number of ground users, Represents the initial dispatch set of hovering point UAVs to all ground users.

7. The method for jointly allocating UAV trajectories and resources according to claim 6, characterized in that: In each iteration of step 4, the concave approximation function of the lower bound of the throughput of the drone to the ground user at the hovering point is obtained by the convex approximation method: Among them, k represents the number of the ground user, i represents the hovering point number, r represents the iteration number; Q represents the set of flight trajectory points of the UAV, Λ represents the hovering time set of the UAV, and A hov represents the set of scheduling coefficients of the UAV at the hovering point for all ground users; q i represents the i-th hovering point of the drone, λ i Represents the drone at the i-th hovering point q i The hovering time at a i,k Represents the drone at the i-th hovering point q i Scheduling coefficient for the kth ground user; d k (q i ) represents the drone at the i-th hovering point q i The distance to k ground users; Represents the rth iteration by using the convex approximation method to obtain the drone's hovering point q at the i-th point i Concave approximation function for the lower bound of the k-th terrestrial user throughput; Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The first approximation coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The second approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The third approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The fourth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The fifth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The sixth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The seventh approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The eighth approximate coefficient of Represents the number of hovering points q at the i-th iteration i The hovering time period t i Concave approximation function for the lower bound of the kth terrestrial user throughput The ninth approximate coefficient of ; In each iteration of step 4, the concave approximate function of the lower bound of the UAV’s throughput to ground users in the flight segment is obtained by the convex approximation method: Among them, k represents the ground user number, i,j represents the trajectory point number, r represents the iteration number; Q represents the set of UAV trajectory points, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; q i,j represents the jth trajectory point after the i-th hovering point of the drone, q i,j+1 represents the j+1th trajectory point after the i-th hovering point of the UAV; a i,j,k Represents the drone from trajectory point q i,j To trajectory point q i,j+1 Scheduling coefficient for the kth ground user in the flight segment; τ represents the time auxiliary variable introduced, q′ i,j (τ) represents the distance from the UAV to the trajectory point q i,j To trajectory point q i,j+1 The position of the τ variable in the flight segment; d k (q′ i,j (τ)) represents the position of the UAV at q′ i,j (τ) The distance to the kth ground user, d i,j (Q) represents the UAV trajectory point q i,j To trajectory point q i,j+1 The length of the flight trajectory, Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The flight trajectory length d i,j (Q) is the lower bound approximation function; Represents the rth iteration by using the convex approximation method to obtain the UAV from the trajectory point q i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the k-th ground user throughput in the flight segment; Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The first approximation coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The second approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The third approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The fourth approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The fifth approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The sixth approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The seventh approximate coefficient of Represents the number of times the UAV moves from trajectory point q in the rth iteration i,j To trajectory point q i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment The eighth approximate coefficient; V represents the maximum flight speed of the drone; Step 4 defines the data transfer target for each iteration as follows: Among them, r represents the iteration number; D (r) represents the auxiliary variable introduced in the rth iteration, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly Represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; max means maximization; The convex data transmission constraint for each iteration described in step 4 is defined as: Where k represents the number of the ground user, i, j represents the number of trajectory points, K represents the number of ground users, N represents the number of turning points between two hovering points, and r represents the number of iterations; D (r) represents the auxiliary variable introduced in the rth iteration, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; Represents the rth iteration by using the convex approximation method to obtain the drone's hovering point q at the i-th point i Concave approximation function for the lower bound of the k-th terrestrial user throughput; Represents the jth trajectory point q obtained by the convex approximation method from the i-th hovering point in the r-th iteration i,j The j+1th trajectory point q after the i-th hovering point i,j+1 The concave approximate function of the lower bound of the k-th ground user throughput in the flight segment; The convex optimization solution target of each iteration of the drone described in step 4 is: Ω(Q,Λ)≤T Λ≥0 Where k represents the number of the ground user, i, j represents the number of trajectory points, K represents the number of ground users, N represents the number of turning points between two hovering points, and r represents the number of iterations; D (r) represents the auxiliary variable introduced in the rth iteration, Q represents the set of drone trajectory points, Λ represents the set of drone hovering time, and A hov Represents the set of scheduling coefficients of the UAV at the hovering point for all ground users, A fly represents the set of scheduling coefficients of the UAV for all ground users in the flight segment; Represents the rth iteration by using the convex approximation method to obtain the drone's hovering point q at the i-th point i Concave approximation function for the lower bound of the k-th terrestrial user throughput; Represents the jth trajectory point q of the drone after the i-th hovering point obtained by the convex approximation method in the r-th iteration i,j The j+1th trajectory point q after the i-th hovering point i,j+1 The concave approximate function of the lower bound of the kth ground user throughput in the flight segment; Ω(Q,Λ) represents the total mission time of the UAV, T represents the given maximum mission time; A hov,k represents the set of scheduling coefficients of the UAV at the hovering point for the kth ground user, A fly,k Represents the set of scheduling coefficients of the UAV for the kth ground user during the flight segment; max represents maximization.

8. The method for jointly allocating UAV trajectories and resources according to claim 7, characterized in that: The optimized solution for each iteration in step 5 is: Among them, Q (r) represents the optimal solution of the set of UAV trajectory points at the rth iteration, Λ (r) represents the optimal solution of the drone hovering time set for the rth iteration, represents the optimal solution of the scheduling coefficient set of the hovering point UAV for all ground users in the rth iteration, Represents the optimal solution of the set of scheduling coefficients of the UAV for all ground users in the flight segment of the rth iteration.

9. The method for jointly allocating UAV trajectories and resources according to claim 8, characterized in that: The change in the auxiliary variable in step 6 is less than the iteration threshold. The specific judgment process is as follows: D (r) -D (r-1) <e Among them, D (r) The auxiliary variable representing the rth iteration is obtained by step 4, D (r-1) The auxiliary variable representing the r-1th iteration is calculated in step 4, and ε represents the iteration threshold; The optimal set of drone trajectory points in step 6 is Q * ; The optimal hovering time set in step 6 is Λ * ; The optimal scheduling coefficient set of the drone at the hovering point for all ground users in step 6 is The optimal scheduling coefficient set of the UAV for all ground users in the flight segment described in step 6 is Output the optimal set of drone trajectory points, hovering time set, the set of scheduling coefficients of drones for all ground users at the hovering point, and the set of scheduling coefficients of drones for all ground users in the flight segment 10. A computer-readable medium, characterized in that It stores a computer program executed by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the method according to any one of claims 1 to 9.

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