UAV base station safety energy efficiency optimization method and device

Abstract

The present invention discloses a method and device for optimizing the safety and energy efficiency of unmanned aerial vehicle (UAV) base stations. The method comprises: constructing a first model of the air-to-ground channel gain of the UAV base station using the Rice distribution, and constructing a second model of the ground channel gain of the UAV base station using the Rayleigh distribution; determining a safety capacity function including the transmission power and an energy consumption function capable of characterizing the trajectory of the UAV based on the model; establishing a safety and energy efficiency optimization equation with the transmission power and trajectory as constraint variables; converting the safety and energy efficiency optimization equation into a convex transformation equation of a joint continuous convex approximation function and / or a differential convex function, and solving the convex transformation equation; and obtaining the optimized transmission power and trajectory using an iterative algorithm based on the solution of the convex transformation equation. Through the above scheme, the present invention can obtain a global approximate optimal solution for maximizing safety and energy efficiency with a relatively small number of iterations, thereby achieving an effective compromise and optimization between physical layer security and energy consumption.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and device for optimizing the safety and energy efficiency of an unmanned aerial vehicle (UAV) base station. Background Art

[0002] Compared to traditional wireless communication systems, drone communication systems offer broad application prospects, but they also present significant challenges. The most significant issue is limited energy supply. A drone's entire energy consumption is derived from its onboard battery, which typically has limited capacity and requires the drone to return to the ground periodically. Consequently, information transmission via drones is typically discontinuous, requiring multiple mission cycles to provide communication services. This significantly limits the amount of information and energy that can be transmitted within a single mission cycle.

[0003] At the same time, due to the openness, instability and inherent broadcast characteristics of wireless channels, when using wireless communications for secure transmission and communication, there is often a very serious threat of confidential information leakage. On the one hand, it is increasingly difficult to ensure security using traditional encryption methods, and on the other hand, existing encryption algorithms are also difficult to implement.

[0004] Although CN112887993 A discloses a solution to the imbalance between safety performance and energy efficiency performance, when it is applied to the existing free space path loss channel model in urban / suburban areas, the results obtained may be inaccurate because it ignores random shadows and small-scale fading and does not take shadow factors into account. In urban areas, signal propagation will be hindered by obstacles (such as buildings, etc.), which will have an adverse impact on both signal transmission security and drone energy efficiency. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method and device for optimizing the safety and energy efficiency of a drone base station that overcomes the above problems or at least partially solves the above problems.

[0006] According to one aspect of the present invention, a method for optimizing the safety and energy efficiency of a drone base station is provided, the method comprising:

[0007] The first model of the air-to-ground channel gain of the UAV base station is constructed using the Rice distribution, and the second model of the ground channel gain of the UAV base station is constructed using the Rayleigh distribution;

[0008] Based on the first model and the second model, respectively determining a safety capacity function including a transmission power and an energy consumption function capable of characterizing the trajectory of the UAV;

[0009] Based on the safety capacity function and the energy consumption function, establishing a safety energy efficiency optimization equation with transmission power and trajectory as constraint variables;

[0010] Converting the safety energy efficiency optimization equation into a convex transformation equation combining continuous convex approximation functions and / or differential convex functions, and solving the convex transformation equation;

[0011] Based on the solution of the convex transformation equation, an iterative algorithm is used to obtain the optimized transmission power and trajectory.

[0012] Optionally, the first model of the air-to-ground channel gain of the UAV base station is constructed using the Rice distribution, including:

[0013] According to the distance between the UAV base station and the ground source node, the attenuation exponential factor and the additional attenuation factor, the LoS link path loss gain and the NLoS link path loss gain are obtained respectively;

[0014] According to the elevation angle and environmental constant between the UAV base station and the ground source node, the availability probability of the LoS link between the UAV base station and the ground source node is obtained;

[0015] Obtaining an air-to-ground channel path loss according to the LoS link path loss gain, the NLoS link path loss gain, and the LoS link availability probability;

[0016] Based on the air-ground channel path loss and Rice distribution, a first model of the air-ground channel gain vector is obtained;

[0017] The second model of the UAV base station ground channel gain is constructed using Rayleigh distribution, including:

[0018] The ground channel path loss is obtained according to the transmission distance between the ground source node and the ground eavesdropping node and the reference distance path loss index;

[0019] A second model of the terrestrial channel gain vector is obtained according to the terrestrial channel path loss, Rayleigh fading exponent and Rayleigh distribution.

[0020] Optionally, determining a safety capacity function including transmit power includes:

[0021] The first capacity function is obtained based on the transmission power of the ground source node, the path loss between the UAV base station and the ground source node, and the self-interference from the UAV transmitting antenna to the receiving antenna;

[0022] The second capacity function is obtained according to the transmission power of the ground source node, the path loss between the ground source node and the ground eavesdropping node, the transmission power of the UAV base station, and the path loss between the UAV base station and the ground eavesdropping node;

[0023] Obtaining a safety capacity function based on a difference between the first capacity function and the second capacity function;

[0024] If the UAV base station is a fixed-wing UAV base station, then determining an energy consumption function capable of characterizing the trajectory of the UAV includes:

[0025] The energy consumption function is determined based on the payload of the UAV, the flight speed and acceleration of the UAV, constant parameters related to the UAV wing area, air density, and UAV weight, as well as the change in the kinetic energy of the UAV.

[0026] Optionally, based on the safety capacity function and the energy consumption function, a safety energy efficiency optimization equation with transmit power and trajectory as constraint variables is established, including:

[0027] Determine the flight trajectory of each time slot based on the speed and acceleration of the drone in each time slot within a preset time period and form a trajectory set;

[0028] A power set is formed based on the transmission power of the ground source node and the UAV base station in each time slot within the preset time period;

[0029] Under the constraints of the trajectory set and the power set, a safety energy efficiency optimization equation is constructed with the maximum value of the sum of the safety capacity function of each time slot and the maximum value of the sum of the energy consumption function of each time slot within a preset time period.

[0030] Optionally, converting the safety energy efficiency optimization equation into a convex transformation equation combining continuous convex approximation functions and / or differential convex functions, and solving the convex transformation equation includes:

[0031] The safety and energy efficiency optimization equation is decomposed into power optimization equation and trajectory optimization equation through alternating iteration method;

[0032] The power optimization equation is formed by solving the product of the speed of each time slot and the effective safety capacity of each ground source node within the preset time period;

[0033] The trajectory optimization equation is formed by solving the quotient of the effective safety capacity of each ground source node and the energy consumption function of each time slot.

[0034] Optionally, converting the safety energy efficiency optimization equation into a convex transformation equation combining continuous convex approximation functions and / or differential convex functions, and solving the convex transformation equation further includes:

[0035] For the power optimization equation, a differential convex function is used to obtain the transmission power of each time slot of the UAV through iterative calculation;

[0036] For the trajectory optimization equation, a continuous convex approximation function is used to obtain the trajectory of the UAV in each time slot through iterative calculation.

[0037] Optionally, based on the calculation result of the convex transformation equation, an iterative algorithm is used to obtain an optimized transmission power and trajectory, including:

[0038] Step 1: Initialize the transmit power set, trajectory set, and auxiliary variables, and set i = 0 and the iteration accuracy threshold;

[0039] Step 2: Given the trajectory variable, calculate the optimal transmit power variable and its auxiliary variables based on the transmit power variable and the power optimization equation;

[0040] Step 3: Given the transmit power variable, calculate the optimal trajectory variable and its auxiliary variables based on the trajectory variable and trajectory optimization equation;

[0041] Step 4: Calculate the safety energy efficiency optimization equation based on the results of steps 2 and 3, and determine whether the value of the safety energy efficiency optimization equation converges to the iteration accuracy threshold. If convergence is established, proceed to step 5; if not, set i = i + 1 and repeat steps 3 and 4;

[0042] Step 5: Obtain the optimized transmit power and trajectory of each time slot.

[0043] According to another aspect of the present invention, a device for optimizing the safety and energy efficiency of a drone base station is provided, the device comprising:

[0044] A channel model construction module, adapted to construct a first model of the air-to-ground channel gain of the UAV base station using Rice distribution, and a second model of the ground channel gain of the UAV base station using Rayleigh distribution;

[0045] an intermediate function determination module, adapted to determine, based on the first model and the second model, a safety capacity function including a transmission power and an energy consumption function capable of characterizing a trajectory of the UAV;

[0046] an optimization equation establishment module, adapted to establish, based on the safety capacity function and the energy consumption function, a safety energy efficiency optimization equation with transmission power and trajectory as constraint variables;

[0047] an optimization equation conversion module, adapted to convert the safety energy efficiency optimization equation into a convex conversion equation combining continuous convex approximation functions and / or differential convex functions, and solve the convex conversion equation;

[0048] The iterative optimization output module is adapted to obtain the optimized transmission power and trajectory using an iterative algorithm based on the solution result of the convex transformation equation.

[0049] According to another aspect of the present invention, there is provided a drone base station, 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;

[0050] 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 drone base station safety and energy efficiency optimization method.

[0051] According to another aspect of the present invention, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned drone base station safety and energy efficiency optimization method.

[0052] The proposed optimization method, based on a combined iteration of power and trajectory, aims to maximize the global safety energy efficiency of a full-duplex UAV system by optimizing both power and trajectory, while ensuring that the flight conditions, average power, and ultimate power are met. In solving the optimal power and trajectory, a block coordinate descent algorithm is used to decompose the original optimization problem into two subproblems: power optimization and trajectory optimization. Convex approximations for each subproblem are derived using the SCA method, resulting in a global approximate optimal solution to the weighted safety energy efficiency maximization problem with a relatively small number of iterations.

[0053] The beneficial effects of the present invention include: the Rice channel model can take into account shadow factors, especially in urban areas, where signal propagation may be hindered by obstacles (such as buildings). Therefore, the channel is divided into LoS ​​links and non-LoS links. Different locations have different characteristics, which effectively improves the accuracy compared to the simplified LoS model. At the same time, because the present invention takes into account the joint performance of the safety performance and energy efficiency performance of the drone base station system, it achieves an effective compromise and optimization of the system's physical layer security and energy consumption.

[0054] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0056] Figure 1 A flow chart of a method for optimizing the safety and energy efficiency of a drone base station according to an embodiment of the present invention is shown;

[0057] Figure 2 A model diagram of a UAV base station system provided by an embodiment of the present invention is shown;

[0058] Figure 3 The following is a schematic diagram showing the structure of a safety and energy efficiency optimization device for a UAV base station provided by an embodiment of the present invention;

[0059] Figure 4 A schematic structural diagram of a drone base station provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0060] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0061] The terms in the present invention are explained as follows: BCD Block Coordinate Descent, BF Beamforming, CN Channel Noise, CGChannel Gain, D2D Device-to-Device, DCDifference of Two-Convex Functions, EEE Energy Efficiency, GEE Global Energy Efficiency, MEP Max–min EE-Optimal Problem, PLS Physical Layer Security, PS Phase Shifts, RSS Received Signal Strength, SCA Successive Convex Approximation, SEE Security Energy Efficiency, SISelf-Interference, SICSelf-Interference Cancellation, SOCS Second Order Cone, SOCP Second Order Cone Programming means second-order cone programming.

[0062] Figure 2The model diagram of the UAV base station system is shown. The system includes a ground source node (S), K single-antenna ground eavesdropping nodes (E), and a full-duplex aerial UAV base station node (U). To improve the system security performance, the full-duplex UAV transmits interference information while receiving the information transmitted by the ground source, thereby reducing the eavesdropping channel quality of the eavesdropping user within a given flight period T. The specific implementation is as follows: Consider a horizontal coordinate that can be expressed as m i ,i∈{s,k}. Assume that the UAV moves from the predetermined initial position q0 to the final position q F The drones are all hovering and communicating in a horizontal plane at a horizontal height H. In practice, the value of H is determined according to the regulations on the flight altitude of the drone. Flying at a constant altitude also helps to avoid unnecessary energy consumption when the aircraft rises or descends. Of course, the scheme disclosed in the present invention can also be extended to situations at different altitudes. The flight time T of the drone can be discretized into N equal lengths, where each time interval is δ = T / N. Therefore, the trajectory of the drone within the time period T can be formed by linking the N optimal discrete trajectory points q[n], n∈N = {1,...,N} through line segments.

[0063] Figure 1 The flowchart of the embodiment of the method for optimizing the safety and energy efficiency of a UAV base station based on Rice channel of the present invention is shown. The method is applied to Figure 2 As shown in the UAV base station system. Figure 1 As shown, the method includes the following steps:

[0064] Step 110, using Rice distribution to construct a first model of the air-to-ground channel gain of the UAV base station, and using Rayleigh distribution to construct a second model of the ground channel gain of the UAV base station.

[0065] The air-ground channel gain in the UAV base station is modeled using Rice distribution, and the ground channel gain in the UAV base station is modeled using Rayleigh distribution.

[0066] Among them, when there is a direct signal with line-of-sight propagation in the received signal, line-of-sight propagation becomes the main receiving component. At the same time, multipath signals arriving randomly at different angles are superimposed on this main signal component. At this time, the envelope of the received signal obeys the Rice distribution.

[0067] Step 120: Determine a safety capacity function including transmit power and an energy consumption function capable of characterizing the trajectory of the UAV based on the first model and the second model, respectively.

[0068] Step 130: Then, based on the safety capacity function and the energy consumption function, a safety energy efficiency optimization equation is established with the transmission power and trajectory as constraint variables.

[0069] The main purpose of the above steps is to take the transmission power of the source node and the UAV node and the constraint variables of the UAV trajectory, propose a safety energy efficiency performance indicator based on power and trajectory constraints, and establish a safety energy efficiency maximization optimization problem model, while the safety capacity and energy consumption can be regarded as intermediate functions.

[0070] Step 140: convert the safety energy efficiency optimization equation into a convex transformation equation combining continuous convex approximation functions and / or differential convex functions, and solve the convex transformation equation.

[0071] In the process of formula derivation, differential convex functions and / or continuous convex approximation methods are used to transform the non-convex optimization subproblems into convex ones, preferably using the first-order Taylor expansion of each variable.

[0072] Step 150: Based on the solution of the convex transformation equation, an iterative algorithm is used to obtain the optimized transmission power and trajectory.

[0073] The above embodiment first models the air-ground channel gain based on the Rice distribution, introduces the non-LoS chain, and establishes a safe and energy-efficient communication system for the UAV base station; under the constraints of power and fixed-wing UAV flight trajectory, the exponential SCA method is used to solve the exponential variables and angle variables, jointly optimize the power of the ground source node and the UAV node and the trajectory of the full-duplex UAV, maximize the safety and energy efficiency performance of the system, ensure the balanced optimization of safety performance and energy efficiency performance, and obtain the global approximate optimization of the weighted safety and energy efficiency maximization problem.

[0074] In particular, modeling the air-ground channel gain based on the Rice distribution is actually more accurate than the simplified LoS model. The Rice channel model takes into account the shadow factor. In urban areas, signal propagation may be hindered by obstacles (such as buildings). Therefore, the channel is divided into LoS ​​links and non-LoS links. Different locations have different characteristics. Compared with the simplified LoS model, the accuracy is effectively improved and an effective compromise between the system's physical layer security and energy consumption is achieved.

[0075] In one or some embodiments, a first model of the air-to-ground channel gain of a UAV base station is constructed using Rice distribution, including:

[0076] According to the distance between the UAV base station and the ground source node, the attenuation exponential factor and the additional attenuation factor, the LoS link path loss gain and the NLoS link path loss gain are obtained respectively;

[0077] According to the elevation angle and environmental constant between the UAV base station and the ground source node, the availability probability of the LoS link between the UAV base station and the ground source node is obtained;

[0078] Obtaining an air-to-ground channel path loss according to the LoS link path loss gain, the NLoS link path loss gain, and the LoS link availability probability;

[0079] Based on the air-ground channel path loss and Rice distribution, the first model of the air-ground channel gain vector is obtained:

[0080]

[0081] Among them, β ui [n] is the path loss in the air-ground channel; if the LoS link between the UAV base station and the ground node is available, the probability is P ui,LoS [n], the path loss gain of the LoS link and the NLoS link is as follows:

[0082]

[0083]

[0084] Among them, d sk It refers to the transmission distance between the ground source node and the ground eavesdropping node. is the distance between the UAV base station and the ground user, c is the additional attenuation factor introduced by the non-direct access, is the attenuation exponential factor, then the free space average path loss of the air-ground channel is as follows:

[0085]

[0086] P ui,LoS ={1+a exp(-b[θ ui [n]-a])} -1 (6)

[0087] Among them, a and b are constants related to the environment, H is the height of the drone from the ground, and the elevation angle between the drone and the ground user is

[0088] The second model of the UAV base station ground channel gain is constructed using Rayleigh distribution, including:

[0089] The ground channel path loss is obtained according to the transmission distance between the ground source node and the ground eavesdropping node and the reference distance path loss index;

[0090] According to the terrestrial channel path loss, Rayleigh fading exponent and Rayleigh distribution, a second model of the terrestrial channel gain vector is obtained:

[0091]

[0092] The path loss in the terrestrial channel, β0 is the path loss at the reference distance, ξ represents Rayleigh fading, the square of which is an exponential random variable, K represents the number of eavesdropping users, and K is a non-negative integer;

[0093] In one embodiment, determining a safety capacity function including transmit power includes:

[0094] According to the transmission power of the ground source node, the path loss between the UAV base station and the ground source node, and the self-interference from the UAV transmitting antenna to the receiving antenna, the first capacity function is obtained:

[0095]

[0096] According to the transmission power of the ground source node, the path loss between the ground source node and the ground eavesdropping node, the transmission power of the UAV base station, and the path loss between the UAV base station and the ground eavesdropping node, the second capacity function is obtained:

[0097]

[0098] A safety capacity function is obtained based on the difference between the first capacity function and the second capacity function.

[0099]

[0100] Among them, I SI represents the self-interference from the UAV transmitting antenna to the receiving antenna, P s [n],P u [n] represents the transmission power of the source node and the UAV base station node, σ 2 represents the noise power, δ = T / N represents the length of each time slot, T is the UAV flight period, and N is the number of time slots.

[0101] If the UAV base station is a fixed-wing UAV base station, then determining an energy consumption function capable of characterizing the trajectory of the UAV includes:

[0102] The energy consumption function is determined based on the drone's payload, the drone's flight speed and acceleration, constant parameters related to the drone's wing area, air density, and drone weight, as well as the drone's kinetic energy change:

[0103]

[0104] in represents the change in kinetic energy of the drone, m represents the mass of the drone, including all payloads, v[n] and a[n] represent the flight speed and acceleration of the drone respectively; g is the acceleration due to gravity, with a standard value of 9.8 m / s 2, c1, c2 are constant parameters related to the UAV wing area, air density, and UAV weight.

[0105] In one or some embodiments, constructing a security energy efficiency optimization equation with the transmit power and the trajectory as constraint variables specifically includes:

[0106] (P1)

[0107] st||a[n]||2≤a max ,n∈N (9b)

[0108] v min ≤||v[n]||2≤v max ,n∈N (9c)

[0109] q[1]=q0, q[N]=q0, v[1]=v[N] (9d)

[0110] v[n+1]=v[n]+a[n]δ,n∈{1,2,...,N-1} (9e)

[0111]

[0112]

[0113]

[0114] Where q[n], v[n], a[n] represent the flight trajectory, velocity and acceleration vector of the UAV respectively, P s [n],P u [n] represents the transmission power of the ground source node and the UAV node, and Represent the power set and trajectory set respectively; v min ,v max ,a max Respectively represent the minimum flight speed, maximum flight speed and maximum flight acceleration of the UAV, P s ave ,P s max and P u ave ,P u max represents the maximum average power and maximum instantaneous power allowed to be transmitted by the source node and the UAV node, and satisfies P s ave ≤P s max and q0 represents the horizontal coordinate of the UAV’s initial flight trajectory, q[1] and q[N] represent the horizontal coordinates of the UAV’s initial time slot and final time slot, respectively; st expresses the constraints on the previous formula, where constraints (9d)-(9f) represent the motion equations of the UAV during flight.

[0115] Preferably, the safety energy efficiency optimization equation is transformed into a convex transformation equation of a combined continuous convex approximation function and / or a differential convex function, and the convex transformation equation is solved using a standard convex optimization package:

[0116] The safety and energy efficiency optimization equation is decomposed into power optimization equation and trajectory optimization equation through alternating iteration method;

[0117] (sub-P1)

[0118] st(9g)and(9h) (10b)

[0119] (sub-P2)

[0120] st(9b)-(9f) (11b)

[0121] Among them, (sub-P1) is the optimization equation for the transmission power variable in each time slot, (sub-P2) is the optimization equation for the UAV trajectory variable in each time slot, and v n =w n / P tot [n] It is worth noting that (sub-P2) is only an identifier used to represent the optimization equation of the trajectory variables, used to distinguish it from (sub-P1), and does not mean that there are actually variables in P2.

[0122] The power optimization subproblem and trajectory optimization subproblem are both non-convex and cannot be solved directly using mathematical solvers. Instead, they require convex transformation using convex optimization methods. Based on this, the next two sections use the SCA method to transform these two optimization subproblems into convex forms, thereby obtaining convex approximations of the power optimization subproblem and the trajectory optimization subproblem. The optimal solution to the original optimization problem is then obtained by alternately iteratively solving the convex approximations of the power optimization subproblem and the trajectory optimization subproblem.

[0123] Furthermore, converting the safety energy efficiency optimization equation into a convex transformation equation combining continuous convex approximation functions and / or differential convex functions, and solving the convex transformation equation using a standard convex optimization package further includes:

[0124] For (sub-P1), using the differential convex function, the transmission power of each time slot of the UAV can be calculated by the following iterative process:

[0125]

[0126] st(9g)and(9h) (12b)

[0127]

[0128] Where g1(P s [n],P u [n])=log2(a n,s P s [n]+b n,s P u [n]+1), τ[n] is an auxiliary variable introduced in the optimization process of the power subproblem; and Both are feasible solutions of formula (9) in time slot n;

[0129] For (sub-P2), using a continuous convex approximation function, the trajectory variables of each time slot of the UAV can be calculated through the following iterative process:

[0130]

[0131] st(9b)-(9f)

[0132] ||v (j) [n]|| 2 +2v (j) [n](v[n]-v (j) [n])≥t 2 [n]n∈N

[0133]

[0134]

[0135]

[0136] l s [n]≤Φ s -X s (||q[n]-m s || 2 -||q (j) [n]-m s || 2 )n∈N

[0137] l k [n]≤Φ k -X k (||q[n]-m k || 2 -||q(j) [n]-m k || 2 )k∈K,n∈N

[0138]

[0139]

[0140]

[0141]

[0142] in, a n =β0P s [n] / (I SI +σ 2 ), c n =β0P u [n] / σ 2 ;

[0143] And ψ[n], z[n], r[n], m[n], l[n], s[n], u[n], t[n] are auxiliary variables, q i [n]、v i [n]、m i [n]、z i [n], ψ i [n], μ i [n] is the feasible solution of the auxiliary variable for the i-th iteration, t[n],ζ[n],s[n],u[n],l s [n],l k [n],η s [n],η k [n],α s [n],α k [n] Auxiliary variables introduced in the optimization process of trajectory subproblem; q s and are the horizontal coordinates of the ground source node and the ground eavesdropping node respectively.

[0144] In one embodiment, based on the calculation result of the convex transformation equation, obtaining the optimized transmit power and trajectory using an iterative algorithm further includes:

[0145] Step 1: Initialize the transmit power set P (i) and the UAV trajectory set Q (i) , and auxiliary variables, and set i = 0 and iteration accuracy ε = 10 -3 ;

[0146] Step 2: Given the trajectory variable Q (i) , in the transmission power variable P (i) Based on the slack variables in the power subproblem, the optimal transmission power variable P is calculated according to formula (12): (i+1) and auxiliary variables;

[0147] Step 3: Given the transmit power variable P (i+1) , in the trajectory variable Q (i) Based on the auxiliary variables in the trajectory subproblem, the optimal trajectory variable Q is calculated according to formula (13) (i+1) and auxiliary variables;

[0148] Step 4: Determine whether the objective function value of formula (9) converges to ε = 10 -3 , that is, whether the difference between the two iteration values ​​is less than the iteration accuracy. If yes, proceed to step 5. If not, set i = i + 1 and repeat steps 3 and 4.

[0149] Step 5: Obtain the optimized transmit power variables and trajectory variables for each time slot.

[0150] Specifically, the effectiveness of the alternating iterative algorithm can be verified and demonstrated through numerical simulation results. For the convenience of simulation and without loss of generality, some of the parameter values ​​are shown in the following table:

[0151]

[0152] Experimental results show that when the number of iterations is 3-6, the global safety energy efficiency tends to be flat and the optimal solution iterative algorithm converges, verifying the effectiveness of the above technical solution.

[0153] Figure 3 FIG. 3 shows a schematic diagram of the structure of an embodiment of a UAV base station safety and energy efficiency optimization device 300 according to the present invention. Figure 3 As shown, the device 300 includes:

[0154] a channel model construction module 310 adapted to construct a first model of the air-to-ground channel gain of the UAV base station using Rice distribution, and to construct a second model of the ground channel gain of the UAV base station using Rayleigh distribution;

[0155] an intermediate function determination module 320 adapted to determine, based on the first model and the second model, a safety capacity function including a transmission power and an energy consumption function capable of characterizing the trajectory of the UAV;

[0156] an optimization equation establishing module 330 adapted to establish, based on the safety capacity function and the energy consumption function, a safety energy efficiency optimization equation with transmit power and trajectory as constraint variables;

[0157] an optimization equation conversion module 340 adapted to convert the safety energy efficiency optimization equation into a convex conversion equation combining continuous convex approximation functions and / or differential convex functions, and solve the convex conversion equation;

[0158] The iterative optimization output module 350 is adapted to obtain the optimized transmission power and trajectory using an iterative algorithm based on the solution result of the convex transformation equation.

[0159] This embodiment proposes modeling the air-ground channel gain based on the Rice distribution. This model is actually more accurate than the simplified LoS model. The Rice channel model takes into account the shadow factor. In urban areas, signal propagation may be hindered by obstacles (such as buildings). Therefore, the channel is divided into LoS ​​links and non-LoS links. Different locations have different characteristics, which effectively improves the accuracy compared to the simplified LoS model. In addition, the above solution combines the joint performance of the safety performance and energy efficiency performance of the drone base station system, achieving an effective compromise between the physical layer security and energy consumption of the system.

[0160] In one embodiment, the channel model construction module 310 uses Rice distribution to construct a first model of the air-to-ground channel gain of the UAV base station, including:

[0161] According to the distance between the UAV base station and the ground source node, the attenuation exponential factor and the additional attenuation factor, the LoS link path loss gain and the NLoS link path loss gain are obtained respectively;

[0162] According to the elevation angle and environmental constant between the UAV base station and the ground source node, the availability probability of the LoS link between the UAV base station and the ground source node is obtained;

[0163] Obtaining an air-to-ground channel path loss according to the LoS link path loss gain, the NLoS link path loss gain, and the LoS link availability probability;

[0164] Based on the air-to-ground channel path loss and Ricean distribution, a first model of the air-to-ground channel gain vector is obtained.

[0165] The channel model construction module 310 uses Rayleigh distribution to construct a second model of the ground channel gain of the UAV base station, including:

[0166] The ground channel path loss is obtained according to the transmission distance between the ground source node and the ground eavesdropping node and the reference distance path loss index;

[0167] A second model of the terrestrial channel gain vector is obtained according to the terrestrial channel path loss, Rayleigh fading exponent and Rayleigh distribution.

[0168] In one embodiment, the intermediate function determination module 320 determines the safety capacity function including the transmit power, including:

[0169] The first capacity function is obtained based on the transmission power of the ground source node, the path loss between the UAV base station and the ground source node, and the self-interference from the UAV transmitting antenna to the receiving antenna;

[0170] The second capacity function is obtained according to the transmission power of the ground source node, the path loss between the ground source node and the ground eavesdropping node, the transmission power of the UAV base station, and the path loss between the UAV base station and the ground eavesdropping node;

[0171] A safety capacity function is obtained based on the difference between the first capacity function and the second capacity function.

[0172] If the UAV base station is a fixed-wing UAV base station, the intermediate function determination module 320 determines an energy consumption function capable of characterizing the trajectory of the UAV, including:

[0173] The energy consumption function is determined based on the payload of the UAV, the flight speed and acceleration of the UAV, constant parameters related to the UAV wing area, air density, and UAV weight, as well as the change in the kinetic energy of the UAV.

[0174] In one embodiment, the optimization equation building module 330 is adapted to:

[0175] Determine the flight trajectory of each time slot based on the speed and acceleration of the drone in each time slot within a preset time period and form a trajectory set;

[0176] A power set is formed based on the transmission power of the ground source node and the UAV base station in each time slot within the preset time period;

[0177] Under the constraints of the trajectory set and the power set, a safety energy efficiency optimization equation is constructed with the maximum value of the sum of the safety capacity function of each time slot and the maximum value of the sum of the energy consumption function of each time slot within a preset time period.

[0178] In one embodiment, the optimization equation conversion module 340 is adapted to:

[0179] The safety and energy efficiency optimization equation is decomposed into power optimization equation and trajectory optimization equation through alternating iteration method;

[0180] The power optimization equation is formed by solving the product of the speed of each time slot and the effective safety capacity of each ground source node within the preset time period;

[0181] The trajectory optimization equation is formed by solving the quotient of the effective safety capacity of each ground source node and the energy consumption function of each time slot.

[0182] In one embodiment, the optimization equation conversion module 340 is further adapted to:

[0183] For the power optimization equation, a differential convex function is used to obtain the transmission power of each time slot of the UAV through iterative calculation;

[0184] For the trajectory optimization equation, a continuous convex approximation function is used to obtain the trajectory of the UAV in each time slot through iterative calculation.

[0185] In one embodiment, the iterative optimization output module 350 is adapted to:

[0186] Step 1: Initialize the transmit power set, trajectory set, and auxiliary variables, and set i = 0 and the iteration accuracy threshold;

[0187] Step 2: Given the trajectory variable, calculate the optimal transmit power variable and its auxiliary variables based on the transmit power variable and the power optimization equation;

[0188] Step 3: Given the transmit power variable, calculate the optimal trajectory variable and its auxiliary variables based on the trajectory variable and trajectory optimization equation;

[0189] Step 4: Calculate the safety energy efficiency optimization equation based on the results of steps 2 and 3, and determine whether the value of the safety energy efficiency optimization equation converges to the iteration accuracy threshold. If convergence is established, proceed to step 5; if not, set i = i + 1 and repeat steps 3 and 4;

[0190] Step 5: Obtain the optimized transmit power and trajectory of each time slot.

[0191] An embodiment of the present invention provides a non-volatile computer storage medium, which stores at least one executable instruction. The computer executable instruction can execute the safety and energy efficiency optimization method of the drone base station based on the Rice channel in any of the above method embodiments.

[0192] Figure 4 The schematic diagram of the structure of the UAV base station embodiment of the present invention is shown, and the specific embodiment of the present invention does not limit the specific implementation.

[0193] like Figure 4 As shown, the drone base station may include: a processor 402 , a communications interface 404 , a memory 406 , and a communication bus 408 .

[0194] Processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. Communication interface 404 is used to communicate with other devices, such as client devices or other server network elements. Processor 402 is used to execute program 410, which may specifically perform the relevant steps of the embodiment of the above-mentioned method for optimizing the safety and energy efficiency of a drone base station based on a Ricean channel for a drone base station.

[0195] Specifically, the program 410 may include program codes, which include computer operation instructions.

[0196] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors may be of the same type, such as one or more CPUs, or different types, such as one or more CPUs and one or more ASICs.

[0197] The memory 406 is used to store the program 410. The memory 406 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0198] Program 410 can be specifically used to enable processor 402 to perform operations corresponding to any of the above-mentioned embodiments of the method for optimizing the safety and energy efficiency of a drone base station based on a Rice channel.

[0199] The algorithm or demonstration provided herein are not inherently relevant to any particular computer, virtual system or other equipment. Various general-purpose systems may also be used together with the teachings based on this. According to the above description, it is apparent that the structure required for constructing this type of system. In addition, the embodiment of the present invention is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0200] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0201] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the present invention.

[0202] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0203] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0204] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components according to an embodiment of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing a part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0205] It should be noted that the above embodiments illustrate rather than limit the invention, and that alternative embodiments may be devised by a person skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments should not be understood as limiting the order of execution unless otherwise specified.

Claims

1. A method for optimizing the safety and energy efficiency of a drone base station, the method comprising: A first model of the air-to-ground channel gain of a UAV base station is constructed using the Rice distribution, including: obtaining the LoS link path loss gain and the NLoS link path loss gain respectively according to the distance, attenuation exponent factor and additional attenuation factor between the UAV base station and the ground source node; obtaining the LoS link availability probability between the UAV base station and the ground source node according to the elevation angle and environmental constant between the UAV base station and the ground source node; obtaining the air-to-ground channel path loss according to the LoS link path loss gain, the NLoS link path loss gain and the LoS link availability probability; obtaining the first model of the air-to-ground channel gain vector based on the air-to-ground channel path loss and the Rice distribution, and constructing the second model of the ground channel gain of the UAV base station using the Rayleigh distribution, including: obtaining the ground channel path loss according to the transmission distance and the reference distance path loss exponent between the ground source node and the ground eavesdropping node; obtaining the second model of the ground channel gain vector according to the ground channel path loss, the Rayleigh fading exponent and the Rayleigh distribution; Based on the first model and the second model, respectively determining a safety capacity function including a transmission power and an energy consumption function capable of characterizing the trajectory of the UAV; Based on the safety capacity function and the energy consumption function, establishing a safety energy efficiency optimization equation with transmission power and trajectory as constraint variables; Converting the safety energy efficiency optimization equation into a convex transformation equation combining continuous convex approximation functions and / or differential convex functions, and solving the convex transformation equation; Based on the solution of the convex transformation equation, an iterative algorithm is used to obtain the optimized transmission power and trajectory.

2. The method according to claim 1, characterized in that Determine the safety capacity function including the transmit power, including: The first capacity function is obtained based on the transmission power of the ground source node, the path loss between the UAV base station and the ground source node, and the self-interference from the UAV transmitting antenna to the receiving antenna; The second capacity function is obtained according to the transmission power of the ground source node, the path loss between the ground source node and the ground eavesdropping node, the transmission power of the UAV base station, and the path loss between the UAV base station and the ground eavesdropping node; Obtaining a safety capacity function based on a difference between the first capacity function and the second capacity function; If the UAV base station is a fixed-wing UAV base station, then determining an energy consumption function capable of characterizing the trajectory of the UAV includes: The energy consumption function is determined based on the drone's payload, the drone's flight speed and acceleration, constant parameters related to the drone's wing area, air density, and drone weight, as well as the drone's kinetic energy change.

3. The method according to any one of claims 1-2, characterized in that Based on the safety capacity function and the energy consumption function, a safety energy efficiency optimization equation is established with the transmission power and trajectory as constraint variables, including: Determine the flight trajectory of each time slot based on the speed and acceleration of the drone in each time slot within a preset time period and form a trajectory set; A power set is formed based on the transmission power of the ground source node and the UAV base station in each time slot within the preset time period; Under the constraints of the trajectory set and the power set, a safety energy efficiency optimization equation is constructed with the maximum value of the sum of the safety capacity function of each time slot and the maximum value of the sum of the energy consumption function of each time slot within a preset time period.

4. The method according to claim 1, wherein Converting the safety energy efficiency optimization equation into a convex conversion equation of a combined continuous convex approximation function and / or a differential convex function, and solving the convex conversion equation, including: The safety and energy efficiency optimization equation is decomposed into power optimization equation and trajectory optimization equation through alternating iteration method; The power optimization equation is formed by solving the product of the speed of each time slot and the effective safety capacity of each ground source node within the preset time period; The trajectory optimization equation is formed by solving the quotient of the effective safety capacity of each ground source node and the energy consumption function of each time slot.

5. The method according to claim 4, characterized in that Converting the safety energy efficiency optimization equation into a convex conversion equation of a combined continuous convex approximation function and / or a differential convex function, and solving the convex conversion equation further includes: For the power optimization equation, a differential convex function is used to obtain the transmission power of each time slot of the UAV through iterative calculation; For the trajectory optimization equation, a continuous convex approximation function is used to obtain the trajectory of the UAV in each time slot through iterative calculation.

6. The method according to claim 4 or 5, characterized in that Based on the calculation results of the convex transformation equation, an iterative algorithm is used to obtain the optimized transmission power and trajectory, including: Step 1: Initialize the transmit power set, trajectory set and auxiliary variables, and set and iterative accuracy threshold; Step 2: Given the trajectory variable, calculate the optimal transmit power variable and its auxiliary variables based on the transmit power variable and the power optimization equation; Step 3: Given the transmit power variable, calculate the optimal trajectory variable and its auxiliary variables based on the trajectory variable and trajectory optimization equation; Step 4: Calculate the safety energy efficiency optimization equation based on the results of step 2 and step 3, and determine whether the value of the safety energy efficiency optimization equation converges to the iteration accuracy threshold. If convergence is established, proceed to step 5. If not, set , repeat steps 3 and 4; Step 5: Obtain the optimized transmit power and trajectory of each time slot.

7. A device for optimizing the safety and energy efficiency of a drone base station, comprising: The channel model construction module is suitable for constructing a first model of the air-to-ground channel gain of the UAV base station using the Rice distribution, including: obtaining the LoS link path loss gain and the NLoS link path loss gain respectively according to the distance, attenuation exponent factor and additional attenuation factor between the UAV base station and the ground source node; obtaining the LoS link availability probability between the UAV base station and the ground source node according to the elevation angle and environmental constant between the UAV base station and the ground source node; obtaining the air-to-ground channel path loss according to the LoS link path loss gain, the NLoS link path loss gain and the LoS link availability probability; obtaining the first model of the air-to-ground channel gain vector based on the air-to-ground channel path loss and the Rice distribution, and constructing the second model of the ground channel gain of the UAV base station using the Rayleigh distribution, including: obtaining the ground channel path loss according to the transmission distance and the reference distance path loss exponent between the ground source node and the ground eavesdropping node; obtaining the second model of the ground channel gain vector according to the ground channel path loss, the Rayleigh fading exponent and the Rayleigh distribution; an intermediate function determination module, adapted to determine, based on the first model and the second model, a safety capacity function including a transmission power and an energy consumption function capable of characterizing a trajectory of the UAV; an optimization equation establishment module, adapted to establish, based on the safety capacity function and the energy consumption function, a safety energy efficiency optimization equation with transmission power and trajectory as constraint variables; an optimization equation conversion module, adapted to convert the safety energy efficiency optimization equation into a convex conversion equation combining continuous convex approximation functions and / or differential convex functions, and solve the convex conversion equation; The iterative optimization output module is adapted to obtain the optimized transmission power and trajectory using an iterative algorithm based on the solution result of the convex transformation equation.

8. A drone base station, 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; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the drone base station safety and energy efficiency optimization method according to any one of claims 1 to 6.

9. A computer storage medium, wherein the storage medium stores at least one executable instruction, wherein the executable instruction causes a processor to perform operations corresponding to the method for optimizing the safety and energy efficiency of a drone base station according to any one of claims 1 to 6.

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