An optimization method for full-duplex fixed-wing UAV relay system under circular trajectory

Abstract

The present invention discloses an optimization method for a full-duplex fixed-wing UAV relay system under a circular trajectory. The optimization method uses the location information of the source node and the destination node, optimizes and adjusts the flight radius, flight speed and flight time of the UAV according to the amount of data that the source node needs to send to the destination node, and minimizes the UAV flight energy consumption under the conditions of meeting the UAV flight speed limit and horizontal turning tilt angle limit, and meeting the system data transmission amount requirements. Simulation experiments show that the optimization method of the present invention has advantages in energy consumption.

Description

Technical Field

[0001] The invention belongs to the technical field of wireless communications, and in particular relates to an optimization method for a full-duplex fixed-wing unmanned aerial vehicle relay system under a circular trajectory. Background Art

[0002] In recent years, UAV cooperative communication technology has become a hot topic in the field of wireless communication. Compared with traditional ground communication, UAV cooperative communication is easy to achieve on-demand allocation, thus having higher communication efficiency; UAVs are highly mobile, so they are more flexible and quick to deploy; the channel links between UAVs and ground terminals are mostly line-of-sight links, which can provide a better channel environment. Therefore, UAVs will play an extremely important role in the field of wireless communication in the future. Their applications mainly include: (1) as temporary base stations; (2) as mobile relays; and (3) for the Internet of Things.

[0003] At present, a large number of literatures have studied the optimization of relay communication system capacity and spectrum efficiency when drones are used as mobile relays. In fact, drones have limited onboard energy, and energy efficiency is considered to be a key issue in drone communications. At present, most studies on drone relay communications consider the decode-and-forward relay communication form, and there are fewer studies on drones using amplify-and-forward relays, especially amplify-and-forward drone relay communications in full-duplex mode, which has not been considered and is worthy of research. On the other hand, the turning radius of fixed-wing drones in circular trajectories is related to the flight speed and the horizontal tilt angle of the drone. This problem has generally been ignored in the past and needs further research. Summary of the invention

[0004] In order to solve the shortcomings and deficiencies of the prior art, the purpose of the present invention is to provide an optimization method for a full-duplex fixed-wing UAV relay system under a circular trajectory. By adjusting the flight radius, flight speed and flight time of the UAV relay system, the UAV flight energy consumption is minimized while complying with the UAV flight speed limit and horizontal turning angle limit, and meeting the system data transmission volume requirements, thereby improving the energy efficiency of the system.

[0005] The present invention is implemented as follows: a method for optimizing a full-duplex fixed-wing UAV relay system under a circular trajectory, which utilizes the location information of a source node and a destination node, optimizes and adjusts the flight radius, flight speed, and flight time of the UAV according to the amount of data that the source node needs to send to the destination node, and minimizes the UAV flight energy consumption while complying with the UAV flight speed limit and horizontal turning inclination angle limit, and meeting the system data transmission amount requirements.

[0006] Preferably, the optimization adjustment formula of the flight radius is:

[0007]

[0008]

[0009] Where Q is the amount of data that the source node S needs to send to the destination node D, β represents the reference value of the channel gain at a distance of 1m, and P S is the transmission power of S, P R is the transmission power of R, H is the flight altitude of the drone relay R, r is the radius of the circular flight trajectory of the drone relay R, L is the distance between S and D, σ 2 is the variance of Gaussian white noise at the R and D nodes, g represents the gravitational acceleration, V min is the minimum flight speed of the drone, e is a natural constant, c1=ηC D0 B / 2, c2=2W 2 / [(πe0A R )ηB], η represents the air density, C D0 represents the zero lift drag coefficient, B represents the wing area, e0 is the span efficiency, W represents the overall weight of the drone, and A R represents the aspect ratio of the drone wing;

[0010] The formula is solved by the golden section one-dimensional search algorithm.

[0011] Preferably, the optimization adjustment formula for the flight speed is:

[0012]

[0013] Where v is the flight speed of the drone relay R, r is the radius of the circular flight trajectory of the drone relay R, g represents the acceleration of gravity, and c1 = ηC D0 B / 2, c2=2W 2 / [(πe0A R )ηB], η represents the air density, C D0 represents the zero lift drag coefficient, B represents the wing area, e0 is the span efficiency, W represents the overall weight of the drone, and A R Indicates the aspect ratio of the drone's wing.

[0014] Preferably, the optimization adjustment formula of the flight time is:

[0015]

[0016] Where Q is the amount of data that the source node S needs to send to the destination node D, T is the time to complete the forwarding of Q data, β is the reference value of the channel gain at a distance of 1m, and P S is the transmission power of S, P Ris the transmission power of R, H is the flight altitude of the drone relay R, r is the radius of the circular flight trajectory of the drone relay R, L is the distance between S and D, σ 2 is the variance of the Gaussian white noise at the R and D nodes, and e is a natural constant.

[0017] Preferably, the optimization method comprises the following steps:

[0018] S1. Use a one-dimensional search algorithm to solve the optimization adjustment formula of the flight radius to obtain the optimal flight radius r of the drone. * ;

[0019] S2, r * Substitute the flight speed optimization adjustment formula and the flight time optimization adjustment formula into the formula to obtain the optimal flight speed v of the drone. * and the optimal flight time T of the drone * ;;

[0020] S3, optimize the optimal flight speed v of the drone * Minimum flight speed V min and maximum flight speed V max Compare; if V min ≤v * ≤V max Then jump to the following step S7;

[0021] S4, if v * <V min , then modify the optimal flight speed, let v * =V min , then jump to the following step S6;

[0022] S5, if v * >V max , then modify the optimal flight speed, let v * =V max ;

[0023] S6, v * Substitute v into the following formula to obtain the modified optimal flight radius r * :

[0024]

[0025] The modified optimal flight radius r * Substitute r into the optimization adjustment formula of the flight time to obtain the modified optimal flight time T * ;

[0026] S7, output optimal flight speed v * , optimal flight radius r* and the optimal flight time T * .

[0027] The present invention overcomes the shortcomings of the prior art and provides an optimization method for a full-duplex fixed-wing UAV relay system under a circular trajectory. The method of the present invention utilizes the location information of a source node and a destination node, and optimizes and adjusts the flight radius, flight speed and flight time of the UAV according to the amount of data that the source node needs to send to the destination node. Under the condition of complying with the UAV flight speed limit and the horizontal turning tilt angle limit, and meeting the system data sending amount requirement, the UAV flight energy consumption is minimized.

[0028] For a full-duplex fixed-wing UAV relay communication system under a circular trajectory, the source node S on the ground needs to send Q data to the destination node D. Assuming that the distance between S and D is L, since S and D are far away, there is no direct link between them. It is necessary to forward the data with the help of a fixed-wing UAV relay R flying at an altitude of H. The UAV relay R works in full-duplex mode (receiving and transmitting signals simultaneously in the same frequency band) and adopts the Amplify-and-Forward (AF) relay protocol. The UAV relay R takes the midpoint of the line connecting the source node S and the destination node D as the center of the circle, r as the radius, and performs circular flight at a uniform speed v. The flight radius r here not only needs to meet the UAV horizontal turning tilt angle limit, but also needs to meet Assuming that both S to R and R to D are line-of-sight (LoS) links, the channel from S to R can be written as: The channel from R to D can be written as: Where α is the wireless channel fading factor, is the distance from the source node S to the relay R, is the distance from relay R to destination node D, t represents time, β represents the reference value of channel gain at a distance of 1 m, and θ is the central angle of the arc length of the drone relay R. Here, it is assumed that the position of the source node S is (0,0,0), the position of the destination node D is (L,0,0), and the starting point of the drone flight is

[0029] It should be noted that the wireless channel fading factor α is usually 2 to 4. Since the channel between the drone and the ground node is composed of a LoS link, α is 2. At this time, the channel from node S to relay R can be written as:

[0030]

[0031] The channel from relay R to node D is:

[0032]

[0033] Since the drone relay R works in full-duplex mode, the wireless signal transmitted by the drone transmitting antenna will be received by the drone receiving antenna. Therefore, the drone needs to use loop interference cancellation (LIC) technology to eliminate the loop interference generated in full-duplex mode. According to the LoS channel and loop interference assumptions, the signal expression received by the drone relay R can be given:

[0034]

[0035] Where P S is the transmission power of S; x S is the transmitted signal of S (assuming the power is 1); P R is the transmit power of R; x R is the transmitted signal of R (assuming the power is 1); h LI is the residual loop interference after LIC; R is the Gaussian white noise received by R (assuming the mean is 0 and the variance is σ 2 ), the drone will receive the signal y R Multiplying by the amplification factor ρ becomes the transmitted signal, that is, x R =ρy R ,here

[0036] Assume that the drone relay R receives the signal y R The processing is ideal enough without any delay, then the signal received by node D is:

[0037]

[0038] where z D is the Gaussian white noise received by D (assuming the mean is 0 and the variance is σ 2 );

[0039] Will Substituting into equation (4) and performing mathematical operations, we can obtain

[0040]

[0041] The received signal-to-noise ratio of node D can be obtained from formula (5), which can be written as:

[0042]

[0043] in

[0044] In this way, the amount of data that the destination node can receive at time t is:

[0045]

[0046] According to the above analysis, the UAV energy consumption optimization problem can be written as

[0047]

[0048] Q D (T)≤0 (8b)

[0049] V min ≤v≤V max (8c)

[0050]

[0051]

[0052] Here, T is the time to complete the forwarding of Q data volume, is the power consumption of a fixed-wing UAV flying in a circular trajectory at a constant speed v and radius r, where g represents the acceleration due to gravity, c1 = ηC D0 B / 2, c2=2W 2 / [(πe0A R )ηB], η represents the air density, C D0 represents the zero lift drag coefficient, B represents the wing area, e0 is the span efficiency, W represents the overall weight of the drone, and A R The aspect ratio of the drone wing, V max and V min are the maximum and minimum flight speeds of the UAV, respectively. Constraint (8d) indicates that the horizontal turning angle of the UAV must be less than or equal to 45°. Constraint (8e) indicates that the projection of the UAV's flight trajectory on the ground should be within the line connecting S and D.

[0053] It should be noted that: the flight speed v and flight radius r of a fixed-wing drone in a circular trajectory should satisfy Where φ is the horizontal turning angle of the drone, and the horizontal turning angle satisfies 0<φ≤45°. Therefore, when the fixed-wing drone flies in a circular trajectory, it must meet Otherwise, the drone will not be able to perform horizontal turns;

[0054] To solve the optimization problem (8), that is, the optimization problem composed of equations (8a), (8b), (8c), (8d) and (8e), we start to simplify the constraint condition (8b); to do this, we first give log2(1+γ D ), which can be written as:

[0055]

[0056] Then according to the inequality And formula (9), we can further find log2(1+γ D ), which can be written as:

[0057]

[0058] because so According to formula (10), we can get log2(1+γ D ), which can be written as;

[0059]

[0060] According to equations (7) and (11), we can get the lower bound of the amount of data that the destination node D can receive at time t:

[0061]

[0062] in

[0063]

[0064] Next, we use equation (12) to obtain Q D The lower bound of (t) To replace Q D (t), then the UAV energy consumption optimization problem, that is, problem (8) can be transformed into

[0065]

[0066]

[0067] V min ≤v≤V max (14c)

[0068]

[0069] It should be noted that: when the lower bound of the data that the destination node D can receive is greater than or equal to Q, the data that the destination node can actually receive will definitely be greater than or equal to Q;

[0070] To solve problem (14), that is, the optimization problem composed of equations (14a), (14b), (14c) and (14d), first, ignore the constraints (14b), (14c) and (14d), calculate the partial derivative of equation (14a) with respect to v and set the partial derivative to 0, and we can get:

[0071]

[0072] Observing formula (15), we find that v is an increasing function of r, that is, as r increases, v also increases, and vice versa. Therefore, when v and r satisfy formula (15), and r tends to infinity, v tends to That is to say, when v and r satisfy equation (15), the maximum value of v is Thus, according to formula (15),

[0073]

[0074] In addition, when v and r satisfy equation (15), It will definitely hold true. Therefore, when v and r satisfy equation (15), the constraint condition equation (14d) can be rewritten as

[0075]

[0076] Substituting equation (16) into (14a), the objective function of the optimization problem (14) can be written as:

[0077] In addition, due to is an increasing function of T and a decreasing function of r. When problem (14) has an optimal solution, constraint equation (14b) will take an equal sign, because if constraint equation (14b) takes a less than sign, then T can be reduced or r can be increased to make Decrease, and thus reduce the value of the objective function formula (14a), so when problem (14) has an optimal solution, the constraint formula (14b) will take an equal sign;

[0078] According to formula (13), when the constraint condition formula (14b) takes the equal sign, we have:

[0079]

[0080] According to the above analysis, question (14) can be rewritten as:

[0081]

[0082]

[0083] The optimization problem (19), that is, the optimization problem composed of equations (19a) and (19b), can obtain the optimal flight radius of the UAV through one-dimensional search. Then, the solution of problem (19) is substituted into equations (15) and (18) to obtain the optimal flight speed and flight time of the UAV. Next, it is necessary to check whether the obtained optimal flight speed of the UAV is greater than or equal to V min and is less than or equal to V max If so, output the optimal solution. If it is less than V minThen modify the optimal flight speed of the drone to or V min , if greater than V max , then the optimal flight speed of the drone is modified to V max , then, substitute the modified optimal flight speed of the UAV into formula (16) to obtain the modified optimal flight radius of the UAV, and finally, substitute the obtained modified optimal flight radius of the UAV into formula (18) to obtain the modified optimal flight time;

[0084] Compared with the shortcomings and deficiencies of the prior art, the present invention has the following beneficial effects: the present invention uses the location information of the source node and the destination node to optimize and adjust the flight speed, flight radius and flight time of the drone according to the maximum and minimum flight speed limits of the drone, the horizontal turning angle limit of the drone, and the amount of data that the source node needs to send to the destination node. This method can minimize the energy consumption of the drone flight while meeting the system's requirements for the amount of data sent; simulation experiments show that this optimization method has advantages in energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 Schematic diagram of the coordinate system of the drone of the present invention;

[0086] Figure 2 Comparison of energy consumption at minimum and maximum flight speeds of the UAV using the optimization method at different Q values;

[0087] Figure 3 The actual amount of data received by the destination node using the optimization method under different Q values ​​and the minimum and maximum flight speeds of the UAV;

[0088] Figure 4 The flight time at the minimum and maximum flight speeds of the UAV using the optimization method at different Q values;

[0089] Figure 5 Comparison of energy consumption at minimum and maximum flight speeds of the UAV using the optimization method under different loop interferences;

[0090] Figure 6 The actual amount of data received by the destination node at the minimum and maximum flight speeds of the UAV using the optimization method under different loop interferences. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0092] The embodiment of the present invention discloses an optimization method for a full-duplex fixed-wing UAV relay system under a circular trajectory. The optimization method utilizes the location information of a source node and a destination node, optimizes and adjusts the flight radius, flight speed and flight time of the UAV according to the amount of data that the source node needs to send to the destination node, and minimizes the UAV flight energy consumption under the condition that the UAV flight speed limit and the horizontal turning tilt angle limit are met, and the system data transmission amount requirement is met.

[0093] like Figure 1 As shown in the figure, the drone relay R always flies at a height of H, and the projection of its flight trajectory on the ground plane is a circle with the midpoint of the line connecting the source node S and the destination node as the center and r as the radius. The projection of the starting point of the drone on the ground plane is the midpoint of the line connecting the source node S and the destination node deviating r toward the S node, and the flight line speed of the drone relay R is v. Drone R forwards the message to the destination node while receiving the signal sent by S. The drone receives the signal and forwards the message at the same time on the same frequency band, that is, it works in full-duplex mode, and the relay forwarding adopts the AF protocol.

[0094] The optimization method includes the following specific steps:

[0095] S1. Use the golden cut one-dimensional search algorithm to solve formula (19) and obtain the optimal flight radius r of the drone *

[0096] In step S1, the one-dimensional search algorithm is used to solve problem (19) to obtain the optimal flight radius r of the UAV. * ;

[0097]

[0098]

[0099] S2, r * Substitute into formula (15) and formula (18) respectively to obtain the optimal flight speed v of the UAV * and the optimal flight time T of the drone * ;

[0100] In step S2, r * Substitute r into equations (15) and (18) to obtain the optimal flight speed v of the UAV: * and the optimal flight time T of the drone * ;;

[0101]

[0102]

[0103] S3, optimize the optimal flight speed v of the drone * Minimum flight speed V min and maximum flight speed V max Compare; if V min ≤v * ≤V max Then jump to the following step S7

[0104] S4, if v * <V min , then modify the optimal flight speed, let v * =V min , then jump to the following step S6;

[0105] S5, if v * >V max , then modify the optimal flight speed, let v * =V max ;

[0106] S6, v * Substituting v into formula (16) obtains the modified optimal flight radius r * :

[0107]

[0108] The modified optimal flight radius r * Substituting r into formula (18) obtains the modified optimal flight time T * :

[0109]

[0110] S7, output optimal flight speed v * , optimal flight radius r * and the optimal flight time T * .

[0111] The above optimization method is simulated and compared with the system power consumption when the drone flies at the minimum speed and maximum speed. The experimental environment is Matlab environment. Assume that the distance between the source node S and the destination node is L = 5000m, and the minimum and maximum speeds of the drone are V min =5m / s, V max =50m / s, the drone flies at a fixed height H = 200m, and the signal transmission power of nodes S and R is P S =P R =-20dBm, the variance of the Gaussian white noise in the environment is σ 2 =-110dBm, unit distance channel gain β = 1, c1 = 9.26 × 10 -4, c2=2250.

[0112] Figure 2 , Figure 3 and Figure 4 The energy consumption comparison between the optimization method and the minimum and maximum flight speeds of the UAV, the actual amount of data received by the destination node, and the flight time of the UAV are given respectively. Here, it is assumed that the loop interference|h LI | 2 =10 -6 ; “Minimum speed flight” and “maximum speed flight” mean that the UAV flies at the minimum and maximum flight speeds respectively, but the flight radius and flight time are optimized. The “proposed algorithm” adopts the proposed optimization method, that is, a joint adjustment algorithm of the UAV flight speed, flight radius and flight time is adopted.

[0113] Figure 2 The energy consumption comparison between the optimization method and the minimum and maximum flight speed of the UAV under different Q values ​​is given. Figure 2 It can be seen that the proposed optimization algorithm saves power consumption significantly compared with “minimum speed flight” and “maximum speed flight”. Figure 3 The actual amount of data received by the destination node at the minimum and maximum flight speeds of the UAV using the optimization method under different Q values ​​is given. Figure 3 It shows that under the three methods, the amount of data that the destination node can receive is greater than the set Q value, which shows the correctness of the proposed optimization method. Figure 4 The flight time of the UAV at the minimum and maximum flight speeds using the optimization method under different Q values ​​is given. Figure 4 It can be seen that the flight time of the three is close, the flight time of the proposed algorithm is in the middle, the flight time of "maximum speed flight" is slightly longer, and the flight time of "minimum speed flight" is slightly shorter. It should be noted that Figure 2 , Figure 3 and Figure 4 Under the adopted parameters, the flight radii of the proposed optimization algorithm, “minimum speed flight” and “maximum speed flight” are 241.8m, 27.57m and 371.4m respectively; the flight speeds are 27.42m / s, 5m / s and 50m / s respectively.

[0114] Figure 5 and Figure 6 The energy consumption comparison of the optimization method and the minimum and maximum flight speed of the UAV under different loop interference and the actual data volume received by the destination node are given respectively. Here, it is assumed that the data volume Q = 100. Figure 5 It can be seen that the proposed optimization algorithm saves power consumption significantly compared with “minimum speed flight” and “maximum speed flight”. Figure 6The actual amount of data received by the destination node under different loop interferences using the optimization method and the minimum and maximum flight speeds of the UAV is given. Figure 6 It shows that under the three methods, the amount of data that the destination node can receive is greater than the set Q value, which further illustrates the correctness of the proposed optimization method. Figure 5 and Figure 6 Under the adopted parameters, the flight radii of the proposed optimization algorithm, “minimum speed flight” and “maximum speed flight” are 241.8m, 27.57m and 371.4m respectively; the flight speeds are 27.42m / s, 5m / s and 50m / s respectively; and the flight times are 213.7s, 187.1s and 231.7s respectively.

[0115] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimization method for a full-duplex fixed-wing UAV relay system under a circular trajectory, characterized in that: The optimization method uses the location information of the source node and the destination node, and optimizes the flight radius, flight speed and flight time of the UAV according to the amount of data that the source node needs to send to the destination node. Under the conditions of meeting the flight speed limit and horizontal turning angle limit of the UAV and meeting the system data sending amount requirements, the UAV flight energy consumption is minimized. The optimization adjustment formula of the flight radius is: Where Q is the amount of data that the source node S needs to send to the destination node D, β represents the reference value of the channel gain at a distance of 1m, and P S is the transmission power of S, P R is the transmission power of R, H is the flight altitude of the drone relay R, r is the radius of the circular flight trajectory of the drone relay R, L is the distance between S and D, σ 2 is the variance of Gaussian white noise at the R and D nodes, g represents the gravitational acceleration, V min is the minimum flight speed of the drone, e is a natural constant, c1=ηC D0 B / 2, c2=2W 2 / [(πe0A R )ηB], η represents the air density, C D0 represents the zero lift drag coefficient, B represents the wing area, e0 is the span efficiency, W represents the overall weight of the drone, and A R represents the aspect ratio of the drone wing; This formula is solved by the golden section one-dimensional search algorithm; The optimization adjustment formula of the flight speed is: Where v is the flight speed of the drone relay R, r is the radius of the circular flight trajectory of the drone relay R, g represents the acceleration of gravity, and c1 = ηC D0 B / 2, c2=2W 2 / [(πe0A R )ηB], η represents the air density, C D0 represents the zero lift drag coefficient, B represents the wing area, e0 is the span efficiency, W represents the overall weight of the drone, and A R represents the aspect ratio of the drone wing; The optimization adjustment formula of the flight time is: Where Q is the amount of data that the source node S needs to send to the destination node D, T is the time to complete the forwarding of Q data, β is the reference value of the channel gain at a distance of 1m, and P S is the transmission power of S, P R is the transmission power of R, H is the flight altitude of the drone relay R, r is the radius of the circular flight trajectory of the drone relay R, L is the distance between S and D, σ 2 is the variance of Gaussian white noise at the R and D nodes, and e is a natural constant; The optimization method includes the following steps: S1. Use a one-dimensional search algorithm to solve the optimization adjustment formula of the flight radius to obtain the optimal flight radius r of the drone. * ; S2, r * Substitute the flight speed optimization adjustment formula and the flight time optimization adjustment formula into the formula to obtain the optimal flight speed v of the drone. * and the optimal flight time T of the drone * ; S3, optimize the optimal flight speed v of the drone * Minimum flight speed V min and maximum flight speed V max Compare; if V min ≤v * ≤V max Then jump to the following step S7; S4, if v * <V min , then modify the optimal flight speed, let v * =V min , then jump to the following step S6; S5, if v * >V max , then modify the optimal flight speed, let v * =V max ; S6, v * Substitute v into the following formula to obtain the modified optimal flight radius r * : The modified optimal flight radius r * Substitute r into the optimization adjustment formula of the flight time to obtain the modified optimal flight time T * ; S7, output optimal flight speed v * , optimal flight radius r * and the optimal flight time T * .

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