Unmanned aerial vehicle speed and resource allocation optimization method and system in frequency spectrum sharing system

Abstract

The invention discloses an unmanned aerial vehicle speed and resource allocation optimization method and system in a spectrum sharing system, and belongs to the technical field of communication. The optimization method comprises the following steps: on the basis of a frequency spectrum sharing system of a multi-user multi-channel FDMA communication network, under interference constraint, flight constraint, transmitting power constraint and channel allocation constraint, constructing a joint optimization problem by taking maximization of the average transmission rate of secondary users as a target; decomposing the joint optimization problem into an unmanned aerial vehicle track and speed optimization sub-problem, a power distribution optimization sub-problem and a channel distribution optimization sub-problem; the three optimization sub-problems are solved alternately and iteratively, and a final solution is obtained; wherein the flight path and speed optimization sub-problem of the unmanned aerial vehicle is solved by adopting SCA and convex optimization, and the flight path and speed of the unmanned aerial vehicle are obtained; solving the power distribution optimization sub-problem by adopting a water injection algorithm to obtain power distribution of the secondary users; and solving the channel allocation optimization sub-problem by adopting an MIP algorithm to obtain frequency spectrum shared channel allocation.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a method and system for optimizing the speed and resource allocation of unmanned aerial vehicles in a spectrum sharing system. Background Art

[0002] With the rapid development of wireless communication technology and the widespread adoption of drones, spectrum sharing systems have shown great potential for improving communication resource utilization. Drones, with their flexible maneuverability, low cost, and rapid deployment capabilities, have been widely used in emergency communications, environmental monitoring, logistics distribution, and military reconnaissance. However, the large-scale application of drone communications also brings challenges such as limited spectrum resources, increased channel interference, and insufficient adaptability to dynamic environments. Given limited spectrum resources, how to efficiently coordinate spectrum sharing between drones and primary users to reduce interference has become a key research issue. Optimizing drone transmit power to reduce interference is a hot topic in many studies. Coordinating the optimization of drone flight speed and channel allocation to balance communication quality, resource utilization, and mission efficiency is a research direction with important theoretical and practical value. Spectrum sharing technology can significantly improve spectrum efficiency and alleviate spectrum resource shortages by allowing multiple communication systems or users to dynamically share the same frequency band. Consequently, much research has focused on spectrum resource allocation in spectrum sharing.

[0003] However, the mobility of drones causes their communication link states to change rapidly over time, making traditional static or semi-static spectrum allocation methods ill-suited to this highly dynamic environment. Furthermore, the drone's flight speed affects the transmission rate, channel allocation strategy, and interference levels with base stations (BSs) or ground users during its flight cycle. More importantly, existing research often fails to fully consider the dynamic heterogeneity of wireless channels in both temporal and spatial dimensions, including key factors such as time-varying channel state information, three-dimensional spatial interference distribution, and power allocation. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method and system for optimizing the speed and resource allocation of drones in a spectrum sharing system, which solves the problems in the existing technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The method for optimizing the speed and resource allocation of drones in a spectrum sharing system includes the following steps:

[0007] A spectrum sharing system based on a multi-user multi-channel FDMA communication network is constructed with the goal of maximizing the average transmission rate of secondary users under interference constraints, flight constraints, transmit power constraints, and channel allocation constraints.

[0008] Decomposing the joint optimization problem into: UAV trajectory and speed optimization sub-problem, power allocation optimization sub-problem and channel allocation optimization sub-problem;

[0009] The three optimization subproblems are solved alternately and iteratively to obtain the final solution. SCA and convex optimization are used to solve the UAV flight trajectory and speed optimization subproblems to obtain the UAV's flight trajectory and speed. The water injection algorithm is used to solve the power allocation optimization subproblem to obtain the power allocation of secondary users. The MIP algorithm is used to solve the channel allocation optimization subproblem to obtain the channel allocation for spectrum sharing.

[0010] Furthermore, the spectrum sharing system includes a base station (BS), a drone (UAV) and several ground users, where the ground users include K secondary users and M primary users (K≤M). The drone flies from a starting point to an end point, sharing the spectrum with the BS to complete data transmission tasks with the secondary users. The secondary users communicate with the drone through frequency division multiplexing technology, and the drone allocates channels to the secondary users based on the quality of the communication channels with them.

[0011] Furthermore, the spectrum sharing system utilizes blockchain architecture to ensure the security and privacy of data and user information, and utilizes smart contracts to automatically execute spectrum sharing.

[0012] Furthermore, the joint optimization problem P1 is:

[0013] P1:

[0014] stC1:||q[n+1]-q[n]||≤V max δ,1≤n≤N-1

[0015] C2:||q[1]||=q I ,||q[N]||=q F

[0016] C3:0≤V[n]≤V max

[0017] C4:0≤a[n]≤a max

[0018] C5:

[0019] C6:1≤c k [n]≤M,c k [n]∈Z

[0020] C7:

[0021] C8:0≤P k [n]≤P max

[0022] C9:E uav ≤E max

[0023] C10:

[0024] Among them, q represents the UAV trajectory optimization variable, q={q[1],...,q[N]}; V represents the UAV speed optimization variable, V={V[1],...,V[N]}; P represents the power allocation optimization variable, P={P[1],...,P[N]}, where P[n]={P1[n],...,P K [n]}; C represents the channel allocation optimization variable, C={C[1],...,C[N]}, where C[n]={c1[n],...,c K [n]}; N represents the total number of time slots, K represents the number of secondary users, R k [n] represents the transmission rate between the UAV and the secondary user k, q[n] represents the two-dimensional projection coordinates of the UAV in the nth time slot, V max represents the maximum flight speed of the UAV, δ is the time slot length, q I represents the initial position of the UAV, q F Indicates the final position of the drone, a max represents the maximum acceleration of the drone, a[n] represents the speed change between time slots, c k [n] represents the number of channel allocations, Z represents an integer, P max is the maximum transmission power of the UAV, P k [n] is the transmission power allocated by the UAV to secondary user k, E uav is the kinetic energy consumption of the UAV, E max is the maximum energy consumption of the UAV, γ thr is the interference threshold to the primary user; represents the maximum interference caused by the UAV to the primary user when communicating on the shared channel; C1 is the maximum displacement constraint of the UAV in a time slot; C2 is the starting and ending point constraints of the UAV; C3 is the speed constraint of the UAV; C4 is the acceleration constraint of the UAV; C5 and C6 are the constraints of the channel allocation coefficient; C7 and C8 are power limits; C9 is the kinetic energy consumption limit of the UAV; C10 is the constraint limit of the interference of UAV spectrum sharing on the primary user.

[0025] Furthermore, the UAV trajectory and speed optimization sub-problem P2 is:

[0026] P2:

[0027] stC1:||q[n+1]-q[n]||≤V max δ

[0028] C2:||q[1]||=q I ,||q[N]||=q F

[0029] C3:0≤V[n]≤V max

[0030] C4:0≤a[n]≤a max

[0031] C9:E uav ≤E max

[0032] C10:

[0033] Among them, R s is the average transmission rate of the secondary user;

[0034] The power allocation optimization sub-problem P3 is:

[0035] P3:

[0036] stC7:

[0037] C8:0≤P k [n]≤P max

[0038] C10:

[0039] The channel allocation optimization sub-problem P4 is:

[0040] P4:

[0041] stC5:

[0042] C6:1≤c k [n]≤M,c k [n]∈Z

[0043] Furthermore, when solving the channel allocation optimization sub-problem, the best adaptation decreasing algorithm is used to implement channel matching based on spatial distance, and the steps include:

[0044] 1) According to the distance between the main user and the drone Sort the channel set C in non-increasing order;

[0045] 2) According to the optimized power P={P1,...,P K Sort the secondary user set S in non-increasing order of};

[0046] 3) Finally, the user set S and the channel set C are matched.

[0047] The UAV speed and resource allocation optimization system in the spectrum sharing system includes:

[0048] Optimization problem construction module: Based on a spectrum sharing system with multiple users and multiple channels in a multi-user FDMA communication network, a joint optimization problem is constructed with the goal of maximizing the average transmission rate of secondary users under interference constraints, flight constraints, transmit power constraints, and channel allocation constraints.

[0049] Optimization problem decomposition module: decomposes the joint optimization problem into: UAV trajectory and speed optimization sub-problems, power allocation optimization sub-problems, and channel allocation optimization sub-problems;

[0050] Problem-solving module: The three optimization subproblems are solved alternately and iteratively to obtain the final solution. SCA and convex optimization are used to solve the drone flight trajectory and speed optimization subproblems, obtaining the drone's flight trajectory and speed. The water injection algorithm is used to solve the power allocation optimization subproblem and obtain the power allocation for secondary users. The MIP algorithm is used to solve the channel allocation optimization subproblem and obtain the channel allocation for spectrum sharing.

[0051] A computer storage medium stores a readable program, which, when executed by a processor, can execute the above-mentioned method for optimizing the speed and resource allocation of drones in a spectrum sharing system.

[0052] An electronic device comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0053] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned method for optimizing drone speed and resource allocation in a spectrum sharing system.

[0054] A computer program product includes computer instructions, which instruct a computing device to perform operations corresponding to the above-mentioned method for optimizing drone speed and resource allocation in a spectrum sharing system.

[0055] Beneficial effects of the present invention:

[0056] 1. This invention constructs a spectrum sharing system for a multi-user, multi-channel FDMA communication network. The system includes a base station (BS), an unmanned aerial vehicle (UAV), multiple secondary users, and a primary user, where the number of primary users outnumbers the number of secondary users. The UAV flies at variable speeds from a starting point to a destination, simultaneously communicating with multiple secondary users within the area. These secondary users share several channels via FDMA. Furthermore, each node in the system forms a blockchain network, ensuring the security of spectrum transactions and information privacy through a distributed architecture.

[0057] 2. Under interference constraints, flight constraints, transmit power constraints, and channel allocation constraints, a joint optimization problem for drone trajectory, velocity, power allocation, and channel allocation was constructed. The optimization goal was to maximize the average transmission rate of the secondary user system. Because the problem is non-convex, it is divided into sub-problems: drone trajectory and velocity optimization, power allocation optimization, and channel allocation optimization. This approach reduces the complexity of solving non-convex and multivariable coupled problems, and achieves higher accuracy and lower algorithmic complexity than heuristic algorithms.

[0058] 3. The present invention alternately iterates the three optimization sub-problems to obtain the final solution, wherein SCA and convex optimization are used to solve the UAV flight trajectory and speed optimization sub-problems, and the first-order Taylor expansion of the objective function is approximated as a convex problem for solution; the MIP algorithm is used to solve the channel optimization sub-problem, by relaxing the integer optimization problem into a continuous variable optimization problem, and then taking subsequent processing to solve it. In addition, the present invention proposes a spatial distance-based channel matching algorithm (Spatial Distance-Based Channel Matching, SDCM) for shared channel matching between secondary users and primary users to reduce interference to primary users; and a water injection algorithm is used to solve the power allocation optimization sub-problem. This method can be solved directly using convex optimization tools, and the optimal solution can be solved using currently mature optimization technology.

[0059] 4. The present invention designs a smart contract to assist spectrum trading and designs a transaction transfer module based on the transmission rate of the secondary user shared channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0061] Figure 1 This is a flow chart of the method for optimizing the speed and resource allocation of UAVs in the spectrum sharing system of the present invention;

[0062] Figure 2 It is a schematic diagram of the structure of the spectrum sharing system of the present invention;

[0063] Figure 3 Schematic diagram of trajectory optimization of the optimization method of the present invention;

[0064] Figure 4 2. It is a schematic diagram of the performance simulation of the optimization method of the present invention under different transmission powers;

[0065] Figure 5 Schematic diagram of simulation of the optimization method of the present invention under different channel bandwidth performance;

[0066] Figure 6 Schematic diagram of the performance simulation of the optimization method of the present invention under different interference thresholds;

[0067] Figure 7 It is a schematic diagram of energy consumption performance simulation of the optimization method of the present invention under different flight cycles. DETAILED DESCRIPTION

[0068] 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 any creative efforts shall fall within the scope of protection of the present invention.

[0069] Example 1

[0070] like Figure 1 As shown in the figure, the method for optimizing the speed and resource allocation of UAVs in the spectrum sharing system includes the following steps:

[0071] S1, a spectrum sharing system based on a multi-user multi-channel FDMA communication network, constructs a joint optimization problem with the goal of maximizing the average transmission rate of secondary users under interference constraints, flight constraints, transmit power constraints and channel allocation constraints;

[0072] like Figure 2 As shown, the spectrum sharing system includes a base station (BS), a drone (UAV), and several ground users, where the ground users include K secondary users and M primary users, with K ≤ M. The drone flies from a starting point to an end point at an altitude of H, sharing the spectrum with the BS to complete the data transmission task with the secondary users. The secondary users communicate with the drone through frequency division multiplexing (FDMA), and the drone allocates channels to the secondary users based on the quality of the communication channel with the secondary users. Since spectrum sharing between the drone and the BS involves spectrum transactions, the present invention utilizes a blockchain architecture to ensure the security and privacy of data and user information, and utilizes smart contracts to automate the spectrum sharing process.

[0073] Smart contracts are used to execute the various steps of spectrum sharing. The design of smart contracts is as follows:

[0074] (1) Register: This function is used to verify the node identity and enable each user to participate in spectrum sharing through registration;

[0075] (2) setValue (initialization): This function can only be called by the BS. This function is used by the BS to set the initialization parameters of spectrum sharing;

[0076] (3) Deposit Funds: The secondary user can use the deposit Funds function to pay a deposit to the smart contract before using the spectrum, the amount is μlb c T, where μ is the weighting coefficient, l is the number of channels, and b c is the channel bandwidth, and T is the flight period of the drone. At this point, the transaction status is false;

[0077] (4) infoRecord: This function is to obtain relevant information about spectrum sharing and broadcast it to the blockchain network;

[0078] (5) Refund (return deposit): This function is the second stage of the transaction. The smart contract will charge the secondary user spectrum sharing fee, the amount of which is Where λ is the weighting coefficient. If the drone deposit is successfully collected, it will be returned. Upon successful completion of this function, the transaction status becomes true, and the transaction information is published to the blockchain network by the smart contract.

[0079] The various model mechanisms involved in this system are as follows:

[0080] 1) UAV flight model:

[0081] The flight period T is divided into N time slots, each of which is δ = T / N. Assume that the position of the drone does not change within a time slot. The two-dimensional projection coordinates of the drone in the nth time slot are expressed as q[n] = (x[n], y[n]), n = 1, ..., N; the maximum flight speed of the drone is V max , when the speed of the time slot is v[n], the following constraints should be met:

[0082] ||q[n+1]-q[n]||≤V MAX δ,1≤n≤N-1

[0083] q[1]=q I

[0084] q[N]=q F

[0085] Among them, this constraint means that the displacement of the UAV in one time slot is not greater than the maximum displacement. I represents the initial position of the UAV, q F Indicates the final position of the drone.

[0086] During the flight of the drone, different time slots have different flight speeds, which will affect the transmission rate between the entire mission cycle and the user. The speed of the drone can be decomposed into V x and V y Two components, let the velocity component of the drone in the x direction be V x , the velocity component in the y direction is V y . V x and V y is the optimization variable used to control the movement of the UAV in each time slot. x and V y , the trajectory of the UAV can be adjusted to maximize the communication rate while satisfying the speed constraint. The UAV position update formula is

[0087] x[n+1]=x[n]+V x [n]δ

[0088] y[n+1]=y[n]+V y [n]δ

[0089] Where x[n] is the horizontal coordinate of the UAV at the nth time slot, and y[n] is the vertical coordinate of the UAV at the nth time slot...; the speed change between time slots is not greater than the maximum acceleration a of the UAV. max .

[0090] 2) Communication model:

[0091] The secondary user communicates with the drone via frequency division multiplexing (FDMA). Assume that the bandwidth shared by the secondary user and the primary user is B, and the total bandwidth is divided into M channels (the number of channels is the same as the number of primary users, and each primary user occupies one channel), and the bandwidth of each channel is b c . User k shares c k channels with a bandwidth of c k b c The communication between the UAV and the secondary user is line-of-sight transmission. In the nth time slot, the channel gain between the UAV and the secondary user k is ||h k [n]|| 2 for:

[0092]

[0093] Among them, q[n] is the two-dimensional coordinate of the UAV, q kis the two-dimensional coordinate of the secondary user; β0 is the channel gain per unit reference distance. Therefore, in the nth time slot, the transmission rate R between the drone and the secondary user k is k [n] is:

[0094]

[0095] in, is the interference of BS to secondary users, is the channel gain from BS to secondary user k, P is the transmission power of the BS to the primary user in the shared channel of the secondary user k; k The transmission power allocated to the secondary user k by the UAV satisfies P max is the maximum transmission power of the UAV, N0 is the power spectrum density of Gaussian white noise;

[0096] The average transmission rate of secondary users R s It can be expressed as:

[0097]

[0098] When UAVs communicate on a shared channel, they can interfere with the primary user. The maximum interference generated is The specific expressions are as follows:

[0099]

[0100] in, is the channel gain between the UAV and the primary user k.

[0101] 3) UAV energy consumption model:

[0102] During the flight, the kinetic energy consumption of the drone is E uav It can be expressed as:

[0103] E uav =E f +E h

[0104] Among them, E f To promote energy consumption, E h is the hovering energy consumption, specifically expressed as:

[0105]

[0106] Among them, δ f and δ h is the time for the UAV to propel and hover, P(v[n]) represents the propulsion power function of the UAV with respect to speed, v[n] represents the speed of the UAV, and P h Indicates the hovering power of the drone.

[0107] The propulsion power function P(v[n]) of the UAV with respect to speed is expressed as:

[0108]

[0109] Among them, P0 and P i is a constant, U tip is the tip speed of the UAV's rotor, v0 is the average rotor induced speed in the hovering state, d0 is the fuselage drag ratio, ρ is the air density, s is the rotor density, and A is the rotor area. Substituting v = 0 into the above formula, the hovering power can be obtained as:

[0110]

[0111] Where Ω is the angular velocity of the UAV rotor blade, R is the rotor radius, W is the weight of the UAV, ψ is the incremental correction factor of the induced power, and δ is the time slot length.

[0112] This paper constructs a joint optimization problem for drone trajectory, speed, power allocation, and channel allocation under interference constraints, flight constraints, transmit power constraints, and channel allocation constraints. The optimization goal of this problem is to maximize the average transmission rate of the secondary user system; the joint optimization problem P1 is:

[0113] P1:

[0114] stC1:||q[n+1]-q[n]||≤V max δ,1≤n≤N-1

[0115] C2:||q[1]||=q I ,||q[N]||=q F

[0116] C3:0≤V[n]≤V max

[0117] C4:0≤a[n]≤a max

[0118] C5:

[0119] C6:1≤c k [n]≤M,c k [n]∈Z

[0120] C7:

[0121] C8:0≤P k [n]≤P max

[0122] C9:Euav ≤E max

[0123] C10:

[0124] Among them, q represents the UAV trajectory optimization variable, q={q[1],...,q[N]}; V represents the UAV speed optimization variable, V={V[1],...,V[N]}; P represents the power allocation optimization variable, P={P[1],...,P[N]}, where P[n]={P1[n],...,P K [n]}; C represents the channel allocation optimization variable, C={C[1],...,C[N]}, where C[n]={c1[n],...,c K [n]}; N represents the total number of time slots, K represents the number of secondary users, R k [n] represents the transmission rate between the UAV and the secondary user k, q[n] represents the two-dimensional projection coordinates of the UAV in the nth time slot, V max represents the maximum flight speed of the UAV, δ is the time slot length, q I represents the initial position of the UAV, q F Indicates the final position of the drone, a max represents the maximum acceleration of the drone, a[n] represents the speed change between time slots, c k [n] represents the number of channel allocations, Z represents an integer, P max is the maximum transmission power of the UAV, P k [n] is the transmission power allocated by the UAV to secondary user k, E uav is the kinetic energy consumption of the UAV, E max is the maximum energy consumption of the UAV, γ thr is the interference threshold to the primary user; represents the maximum interference caused by the UAV to the primary user when communicating on the shared channel; C1 is the maximum displacement constraint of the UAV in a time slot; C2 is the starting and ending point constraints of the UAV; C3 is the speed constraint of the UAV; C4 is the acceleration constraint of the UAV; C5 and C6 are the constraints of the channel allocation coefficient, ensuring that the allocated channels do not exceed the number of channels of the primary user; C7 and C8 are power limits, ensuring that the total power allocated to the secondary user is not greater than the maximum transmit power of the UAV; C9 is the kinetic energy consumption limit of the UAV; C10 is the constraint limit on the interference of UAV spectrum sharing to the primary user.

[0125] S2, decomposing the joint optimization problem into: UAV trajectory and speed optimization sub-problems, power allocation optimization sub-problems, and channel allocation optimization sub-problems;

[0126] Since the joint problem is non-convex, it is divided into UAV trajectory and speed optimization sub-problems, power allocation optimization sub-problems, and channel allocation optimization sub-problems.

[0127] 1) UAV trajectory and speed optimization sub-problem P2:

[0128] P2:

[0129] stC1:||q[n+1]-q[n]||≤V max δ

[0130] C2:||q[1]||=q I ,||q[N]||=q F

[0131] C3:0≤V[n]≤V max

[0132] C4:0≤a[n]≤a max

[0133] C9:E uav ≤E max

[0134] C10:

[0135] Among them, R s is the average transmission rate of the secondary user;

[0136] 2) Power allocation optimization sub-problem P3:

[0137] P3:

[0138] stC7:

[0139] C8:0≤P k [n]≤P max

[0140] C10:

[0141] 3) Channel allocation optimization sub-problem P4:

[0142] P4:

[0143] stC5:

[0144] C6:1≤c k [n]≤M,c k [n]∈Z

[0145] In step S3, the three optimization subproblems are solved alternately and iteratively to obtain the final solution. The UAV flight trajectory and speed optimization subproblems are solved using SCA and convex optimization to obtain the UAV flight trajectory and speed. The power allocation optimization subproblem is solved using the water injection algorithm to obtain the power allocation for the secondary user. The channel allocation optimization subproblem is solved using the MIP algorithm to obtain the channel allocation for spectrum sharing.

[0146] S31, the steps for solving the sub-problems of UAV flight trajectory and speed optimization using SCA and convex optimization are as follows:

[0147] Since the logarithmic term in the objective function leads to non-convexity, it is difficult to solve it directly. This embodiment linearizes the objective function by the Successive Convex Approximation (SCA) method. (l) ,y (l) ) performs a first-order Taylor expansion:

[0148]

[0149] in, The trust region constraint defines the range of changes in the optimization variables in each iteration. In the optimization problem, the C1 constraint is a trust region constraint. Since the position update satisfies the velocity constraint, the velocity constraint implicitly limits the displacement of adjacent points, which is equivalent to an implicit trust region. The optimization problem P5 can be expressed as:

[0150] P5:

[0151] stC1:q[n+1]=q[n]+V[n]δ

[0152] C2:||q[1]||=q I

[0153] C3:||q[N]||=q F

[0154] C4:0≤V[n]≤V max

[0155] C5:0≤a[n]≤a max

[0156] C6:E uav ≤E max

[0157] C7:

[0158] This problem is a convex optimization problem and can be solved using convex optimization tools. At each time slot, the gradient of the objective function is calculated. A convex optimization problem is then constructed. After defining the constraints and variables, the problem is then solved using a convex optimization solver. The trajectory and velocity are updated at each iteration. When the trajectory change is less than the convergence coefficient, the optimal trajectory and velocity sequence for the drone is obtained. The solution involves the following steps:

[0159] (1) Initialize the straight line trajectory;

[0160] (2) Initialize the straight line trajectory;

[0161] (3) Save the last round of trajectory q l =q l-1 ;

[0162] (4) Calculate the distance and gradient between the drone and the user;

[0163] (5) Construct optimization objectives;

[0164] (6) Define optimization variables q, V x , V y ;

[0165] (7) Solve convex optimization problems;

[0166] (8) Update trajectory, q = q l ;

[0167] (9) Update speed, V = V l .

[0168] S32, solving the power allocation optimization sub-problem using a water injection algorithm to obtain the power allocation of the secondary user;

[0169] The water injection algorithm is a classic approach to solving this problem. It first calculates each user's channel, then ranks the channels by quality, starting with the worst channel and gradually raising the water mark until the total power constraint is met. The required power calculation also takes into account the bandwidth weight of each channel. The water injection algorithm solves power allocation problems, essentially a convex optimization problem. Taking the second-order derivative of the transmission rate with respect to power yields:

[0170]

[0171] in, It can be seen from the formula that for P k The second-order derivative of is always negative, so it is a concave function of power. The sum of multiple concave functions is still a concave function, so the objective function of P3 is concave, and the constraints are convex. This problem can be solved using convex optimization tools. The specific steps of problem P3 are as follows:

[0172] (1) Calculate the channel gain of the secondary user;

[0173] (2) Constructing and maximizing the rate objective function;

[0174] (3) Define constraints;

[0175] (4) Constructing a convex optimization problem;

[0176] (5) Solved by convex optimization solver.

[0177] S33, using the MIP algorithm to solve the system allocation optimization sub-problem to obtain the channel allocation of the secondary user.

[0178] The P4 problem is an integer programming problem (MIP). First, an initial relaxation is performed to convert the integer variables into continuous variables. After solving the relaxation problem, if the relaxation solution does not satisfy the integer constraint, branch and bound is used to gradually approximate the integer solution. Branch and bound will first branch and select a non-integer variable x. j Divide the original problem into two sub-problems, and add constraints to sub-problem 1. Add to sub-problem 2 The problem space is recursively partitioned to gradually approach an integer solution. This problem can be solved using a MIP solver. The solution process of P4 is as follows:

[0179] (1) Calculate the distance between the drone and the secondary user;

[0180] (2) Calculate normalized weights;

[0181] (3) Define integer variables;

[0182] (4) Constructing the objective function;

[0183] (5) Solve mixed integer programming problems.

[0184] When solving the channel allocation optimization sub-problem, in order to reduce the interference to the primary user when allocating channels, the best adaption decreasing algorithm is used to implement spatial distance-based channel matching (Spatial Distance-Based Channel Matching, SDCM). The best adaption decreasing algorithm (Best Fit Decreasing, BFD) is often used to solve the packing problem. In the packing problem, a finite set of items must be packed into an infinite set of boxes. Accordingly, in the channel allocation problem of this embodiment, a finite set of items is equivalent to a set of secondary users s, and a set of boxes is equivalent to a set of channels. Unlike the packing problem, this method aims to pack items (secondary users) into a finite number of boxes (channels). Based on these differences, this section designs a BFD-based SDCM to solve the current channel allocation problem. The specific steps of using the best adaption decreasing algorithm to implement spatial distance-based channel matching are as follows:

[0185] 1) First, according to the distance between the main user and the drone Sort the channel set C in non-increasing order;

[0186] 2) Then according to the optimized power P={P1,...,P K Sort the secondary user set S in non-increasing order of};

[0187] 3) Finally, the user set S and the channel set C are matched.

[0188] In this embodiment, the trajectory optimization diagram of the UAV is as follows: Figure 3 As shown in the figure, to maximize the transmission rate of the secondary user, the drone's trajectory is close to the secondary user. During flight, interference control is performed through power control and SDCM methods. In addition, when the drone flies near the user, it slows down until it hovers, and then accelerates to the destination.

[0189] Comparison of secondary user average rates under different transmission powers of drones Figure 4 As shown in the figure, as the UAV's transmit power increases, the total power allocated to secondary users increases, which in turn increases the transmission rate of the secondary user system. Although increasing the UAV's transmit power will provide a higher rate for secondary users, due to interference limitations, the improvement in the average secondary user rate gradually decreases as the UAV's transmit power increases.

[0190] Comparison of secondary user average rates under different channel bandwidths Figure 5As shown in the figure, the average rate of secondary users increases significantly with increasing channel bandwidth. After joint optimization, the number of channels a secondary user receives in each time slot is fixed. Increasing the channel bandwidth directly increases the data transmission rate between the drone and the secondary user, thereby increasing the average rate.

[0191] Comparison of average secondary user rates with different interference thresholds Figure 6 As shown in the figure, reducing the interference threshold significantly reduces the average rate. This is because the smaller the interference threshold, the more the drone needs to reduce its transmit power to mitigate interference for secondary users exceeding the threshold, resulting in a lower rate. This means that the greater the interference that the primary user can tolerate in spectrum sharing, the higher the average rate of the secondary user in spectrum sharing.

[0192] The energy consumption comparison of drones at different working hours is shown in the figure below: Figure 7 As shown in the figure, as the flight cycle lengthens, the drone's propulsion energy consumption remains almost constant, while hovering energy consumption shows a continuous upward trend. When the drone flies near the user, it hovers for a period of time before flying to its destination. Since the drone's starting and ending points remain unchanged, the propulsion time does not change significantly as the flight cycle lengthens. When the flight cycle is short, the drone flies to the user's location to complete communication, and hovering energy consumption is low. As the flight cycle increases, the drone can hover closer to the user for longer periods of time, increasing hovering energy consumption. When the flight cycle is set very long, the drone's hovering time becomes very long, and hovering energy consumption increases accordingly, exceeding the propulsion energy consumption.

[0193] Based on similar inventive concepts, an embodiment of the present invention also provides a computer storage medium storing a readable program. When the program is executed by a processor, it can execute the above-mentioned method for optimizing the speed and resource allocation of drones in a spectrum sharing system.

[0194] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0195] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned load prediction method.

[0196] Based on similar inventive concepts, an embodiment of the present invention also provides a computer program product, including computer instructions, which instruct a computing device to perform operations corresponding to the above-mentioned method for optimizing drone speed and resource allocation in a spectrum sharing system.

[0197] Example 2

[0198] Based on the method for optimizing the speed and resource allocation of drones in a spectrum sharing system proposed in Example 1, this embodiment proposes a system for optimizing the speed and resource allocation of drones in a spectrum sharing system, specifically including:

[0199] Optimization problem construction module: Based on a spectrum sharing system with multiple users and multiple channels in a multi-user FDMA communication network, a joint optimization problem is constructed with the goal of maximizing the average transmission rate of secondary users under interference constraints, flight constraints, transmit power constraints, and channel allocation constraints.

[0200] Optimization problem decomposition module: decomposes the joint optimization problem into: UAV trajectory and speed optimization sub-problems, power allocation optimization sub-problems, and channel allocation optimization sub-problems;

[0201] Problem-solving module: The three optimization subproblems are solved alternately and iteratively to obtain the final solution. SCA and convex optimization are used to solve the drone flight trajectory and speed optimization subproblems, obtaining the drone's flight trajectory and speed. The water injection algorithm is used to solve the power allocation optimization subproblem and obtain the power allocation for secondary users. The MIP algorithm is used to solve the channel allocation optimization subproblem and obtain the channel allocation for spectrum sharing.

[0202] The method of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as an ASIC or FPGA). It will be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the method shown here.

[0203] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for optimizing the speed and resource allocation of drones in a spectrum sharing system, characterized in that: The following steps are involved: A spectrum sharing system based on a multi-user multi-channel FDMA communication network is constructed with the goal of maximizing the average transmission rate of secondary users under interference constraints, flight constraints, transmit power constraints, and channel allocation constraints. Decomposing the joint optimization problem into: UAV trajectory and speed optimization sub-problem, power allocation optimization sub-problem and channel allocation optimization sub-problem; Solve the three optimization sub-problems alternately and iteratively to obtain the final solution; SCA and convex optimization are used to solve the UAV flight trajectory and speed optimization sub-problems to obtain the UAV's flight trajectory and speed; the water injection algorithm is used to solve the power allocation optimization sub-problem to obtain the power allocation of secondary users; the MIP algorithm is used to solve the channel allocation optimization sub-problem to obtain the channel allocation of spectrum sharing.

2. The method for optimizing UAV speed and resource allocation in a spectrum sharing system according to claim 1, characterized in that: The spectrum sharing system includes a base station (BS), a drone (UAV), and several ground users, where the ground users include K secondary users and M primary users (K ≤ M). The drone flies from a starting point to a destination, sharing the spectrum with the BS to complete data transmission tasks with the secondary users. The secondary users communicate with the drone using frequency division multiplexing technology, and the drone allocates channels to the secondary users based on the quality of the communication channel with them.

3. The method for optimizing UAV speed and resource allocation in a spectrum sharing system according to claim 2, wherein: The spectrum sharing system uses blockchain architecture to ensure the security and privacy of data and user information, and uses smart contracts to automatically execute spectrum sharing.

4. The method for optimizing UAV speed and resource allocation in a spectrum sharing system according to claim 2, wherein: The joint optimization problem P1 is: s.t.C1:||q[n+1]-q[n]||≤V max δ,1≤n≤N-1 C2:||q[1]||=q I ,||q[N]||=q F C3:0≤V[n]≤V max C4:0≤a[n]≤a max C6:1≤c k [n]≤M,c k [n]∈Z C8:0≤P k [n]≤P max C9:E uav ≤E max Among them, q represents the UAV trajectory optimization variable, q={q[1],...,q[N]}; V represents the UAV speed optimization variable, V={V[1],...,V[N]}; P represents the power allocation optimization variable, P={P[1],...,P[N]}, where P[n]={P1[n],...,P K [n]}; C represents the channel allocation optimization variable, C={C[1],...,C[N]}, where C[n]={c1[n],...,c K [n]}; N represents the total number of time slots, K represents the number of secondary users, R k [n] represents the transmission rate between the UAV and the secondary user k, q[n] represents the two-dimensional projection coordinates of the UAV in the nth time slot, V max represents the maximum flight speed of the UAV, δ is the time slot length, q I represents the initial position of the UAV, q F Indicates the final position of the drone, a max represents the maximum acceleration of the drone, a[n] represents the speed change between time slots, c k [n] represents the number of channel allocations, Z represents an integer, P max is the maximum transmission power of the UAV, P k [n] is the transmission power allocated by the UAV to secondary user k, E uav is the kinetic energy consumption of the UAV, E max is the maximum energy consumption of the UAV, γ thr is the interference threshold to the primary user; represents the maximum interference caused by the UAV to the primary user when communicating on the shared channel; C1 is the maximum displacement constraint of the UAV in a time slot; C2 is the starting and ending point constraints of the UAV; C3 is the speed constraint of the UAV; C4 is the acceleration constraint of the UAV; C5 and C6 are the constraints of the channel allocation coefficient; C7 and C8 are power limits; C9 is the kinetic energy consumption limit of the UAV; C10 is the constraint limit of the interference of UAV spectrum sharing on the primary user.

5. The method for optimizing UAV speed and resource allocation in a spectrum sharing system according to claim 4, characterized in that: The UAV trajectory and speed optimization sub-problem P2 is: s.t.C1:||q[n+1]-q[n]||≤V max δ C2:||q[1]||=q I ,||q[N]||=q F C3:0≤V[n]≤V max C4:0≤a[n]≤a max C9:E uav ≤E max Among them, R s is the average transmission rate of the secondary user; The power allocation optimization sub-problem P3 is: C8:0≤P k [n]≤P max The channel allocation optimization sub-problem P4 is: C6:1≤c k [n]≤M,c k [n]∈Z。 6. The method for optimizing UAV speed and resource allocation in a spectrum sharing system according to claim 1, wherein: When solving the channel allocation optimization subproblem, the best adaptation decreasing algorithm is used to achieve channel matching based on spatial distance. The steps include: 1) According to the distance between the main user and the drone Sort the channel set C in non-increasing order; 2) According to the optimized power P={P1,...,P K Sort the secondary user set S in non-increasing order of}; 3) Finally, the user set S and the channel set C are matched.

7. UAV speed and resource allocation optimization system in spectrum sharing system, characterized by: include: Optimization problem construction module: Based on a spectrum sharing system with multiple users and multiple channels in a multi-user FDMA communication network, a joint optimization problem is constructed with the goal of maximizing the average transmission rate of secondary users under interference constraints, flight constraints, transmit power constraints, and channel allocation constraints. Optimization problem decomposition module: decomposes the joint optimization problem into: UAV trajectory and speed optimization sub-problems, power allocation optimization sub-problems, and channel allocation optimization sub-problems; Problem solving module: solves the three optimization sub-problems alternately and iteratively to obtain the final solution; SCA and convex optimization are used to solve the UAV flight trajectory and speed optimization sub-problems to obtain the UAV's flight trajectory and speed; the water injection algorithm is used to solve the power allocation optimization sub-problem to obtain the power allocation of secondary users; the MIP algorithm is used to solve the channel allocation optimization sub-problem to obtain the channel allocation of spectrum sharing.

8. A computer storage medium storing a readable program, characterized in that: When the program is executed by the processor, the method for optimizing the speed and resource allocation of a drone in a spectrum sharing system according to any one of claims 1 to 6 can be executed.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the method for optimizing drone speed and resource allocation in a spectrum sharing system according to any one of claims 1 to 6.

10. A computer program product comprising computer instructions, characterized in that The computer instructions instruct the computing device to execute operations corresponding to the method for optimizing the speed and resource allocation of a drone in a spectrum sharing system as described in any one of claims 1 to 6.

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