A differentially coded UAV relay communication system trajectory optimization method

Abstract

The invention discloses a differentially coded unmanned aerial vehicle relay communication system track optimization method, which belongs to the technical field of unmanned aerial vehicle communication. The invention first constructs a differentially coded unmanned aerial vehicle relay communication system model, then transforms the coordinates of each node in the system model according to the proposed unmanned aerial vehicle distance constraint method, and finally optimizes the unmanned aerial vehicle heading angle based on the average output signal-to-noise ratio maximization criterion to obtain the optimal track. The method of the invention can effectively improve the reliability of link transmission of a point-to-point unmanned aerial vehicle relay communication system and reduce the system channel estimation requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle communication, and in particular relates to a differentially coded unmanned aerial vehicle relay communication system track optimization method. Background Art

[0002] Unmanned Aerial Vehicle (UAV) relay communication system is a long-distance wireless communication system with UAVs as relay nodes. Compared with traditional relay communication systems, UAV relay communication has many advantages such as low maintenance cost, flexible deployment, controllable relay position, and rapid construction. Therefore, it has been widely used in military and civilian communication fields. Since the relay nodes are deployed in high-speed flying UAVs, UAV relay communication also has a series of special problems. One of the important problems is the track optimization problem of relay UAVs. The track optimization problem of relay UAVs can be expressed as: during the flight of the relay UAV, how to find an optimal flight path to ensure the transmission reliability of the UAV relay communication link. Since the flight path of the relay UAV has a decisive influence on the transmission reliability of the link of the UAV relay communication system, it is of great significance to carry out research on the track optimization of UAV relay communication.

[0003] A lot of research has been conducted at home and abroad to optimize the trajectory of drone relay communication, with the goal of improving communication performance, confidentiality performance, and energy efficiency. Point-to-point drone relay communication transmission schemes mainly include: single-transmit single-receive transmission scheme, beamforming transmission scheme, space-time block coding transmission scheme, and cooperative space-time block coding transmission scheme. The following describes the defects and shortcomings of these technologies respectively.

[0004] The basic idea of ​​the single-transmit single-receive transmission scheme is that the system consists of three nodes, namely the source node, the relay drone node and the destination node, and each node is equipped with an antenna. There is no direct link between the source node and the destination node. During the communication process, the source node first sends the signal to the drone node, and then the drone forwards the received signal to the destination node after amplification. The disadvantage of this scheme is that the system performance is greatly affected by channel fading and the link reliability is poor.

[0005] The basic idea of ​​the beamforming transmission scheme: The system consists of three nodes, namely the source node, the relay drone node and the destination node. The source node and the destination node are equipped with multiple antennas, and the drone node is equipped with a single antenna. There is no direct link between the source node and the destination node. During the communication process, the source node first beamforms the signal and sends it to the drone node. The drone then amplifies and forwards the received signal to the destination node. Finally, the destination node performs beamforming on the received signal. The defect of this scheme is that both the source node transmitter and the destination node receiver need to obtain accurate channel fading information through channel estimation, which takes up a lot of system overhead and cannot ensure accuracy, so it is difficult to apply in practice.

[0006] The basic idea of ​​the space-time block coding transmission scheme: The system consists of three nodes, namely the source node, the relay drone node and the destination node. The source node and the destination node are equipped with dual antennas, and the drone node is equipped with a single antenna. There is no direct link between the source node and the destination node. During the communication process, the source node first transmits the signal through two transmitting antennas in two consecutive time slots in the form of space-time block coding. Then the drone forwards the received signal to the destination node after amplification. Finally, the destination node performs correlation and merging processing on the received signal. The defect of this scheme is that the channel distance between the ground antenna and the drone is relatively close, resulting in strong correlation, making it difficult to ensure spatial diversity gain.

[0007] The basic idea of ​​the collaborative space-time block coding transmission scheme is that the system consists of four nodes, namely the source node, two relay drone nodes and the destination node. Each node is equipped with a single antenna, and there is no direct link between the source node and the destination node. During the communication process, the source node first transmits two signals in two consecutive time slots, and then the drone decodes the two received signals and forwards them to the destination node in space-time block coding. Finally, the destination node performs correlation merging processing on the received signals. The defects of this scheme are that the system requires channel estimation to obtain channel fading information, and the drone uses decoding and forwarding, which leads to a complex system structure. In addition, the drone distance constraint method uses a no-fly zone constraint method, which is not flexible enough and has insufficient tracking capabilities for moving source nodes. Summary of the invention

[0008] In view of this, the purpose of the present invention is to provide a differentially coded UAV relay communication system trajectory optimization method, which can improve the link transmission reliability of the UAV relay communication system, thereby reducing the system channel estimation overhead and ensuring the flight safety of the UAV.

[0009] In order to achieve the above object, the present invention provides the following technical solutions:

[0010] A differentially coded UAV relay communication system track optimization method comprises the following steps:

[0011] S1. Establish a differentially coded UAV relay communication system model;

[0012] S2, establish a trajectory planning model for the UAV relay communication system based on the average output signal-to-noise ratio maximization criterion;

[0013] S3. Realize the UAV distance constraint through the coordinate transformation method.

[0014] Further, step S1 specifically includes:

[0015] S1.1, the system is composed of a ground mobile user node, a ground base station node, and multiple drone relay nodes;

[0016] S1.2, let the signal be transmitted through a transmitting antenna at a certain power at the source node at time t in a differentially coded manner;

[0017] S1.3, all drone nodes forward the received signals to the destination node using amplification and forwarding at a certain power;

[0018] S1.4. The destination node processes the received signal using a correlation combining method and a differential demodulation method.

[0019] Further, step S2 specifically includes:

[0020] S2.1, solve the distance from the source node and the destination node to each drone;

[0021] S2.2, solve the average output signal-to-noise ratio of the UAV relay communication system at time t;

[0022] S2.3. The optimal heading angle is solved based on the average output signal-to-noise ratio maximization criterion and the optimal trajectory of the UAV is obtained.

[0023] Further, step S3 specifically includes:

[0024] S3.1, solving the virtual node coordinates after the ground node transformation;

[0025] S3.2, replace the ground node coordinates in S2.1 with the virtual node coordinates;

[0026] S3.3. Use the trajectory optimization method after coordinate transformation to obtain the optimal trajectory of the UAV.

[0027] Further, in step S1.2, the jth UAV receives a packet containing x (k) The signal Defined as:

[0028]

[0029] Where j = 1, 2...n represents the drone serial number, P M Indicates the transmit power of the MU node, represents the channel fading coefficient from MU to the jth UAV, Represents the additive white Gaussian noise with a mean of 0 and a variance of N0 introduced at the j-th UAV receiving antenna.

[0030] Further, in step S1.3, the signal received by the BS node from the jth UAV Defined as:

[0031]

[0032] in represents the signal from the jth UAV received by the BS, P U represents the forwarding power of the UAV node. Here, the forwarding powers of the two UAVs are equal. represents the channel fading coefficient from the jth UAV to the BS, Represents the additive white Gaussian noise with mean 0 and variance N0 introduced by the j-th relay link at the BS.

[0033] Further, in step S1.4, the BS combiner outputs x (k) The signal Defined as:

[0034]

[0035] Among them, w j represents the optimal merging weight.

[0036] Furthermore, in step 3.2, by replacing the ground node coordinates in S2.1 with the virtual node coordinates, the signal related to the kth symbol processed by the combiner can be obtained from equation (7), and the average output signal-to-noise ratio of the system after the coordinate transformation is obtained: Defined as:

[0037]

[0038] in, is the long-term average value of the channel fading coefficient from the MU node to the jth UAV node, is the long-term average of the channel fading coefficient from the jth UAV node to the BS node, α represents the path loss factor, and d′ M,j represents the distance from the jth UAV to MUj, d j ' ,B represents the distance from the jth UAV to BSj;

[0039] Assuming that the UAV position is given at time t-Δt, the large-scale fading of the channel at time t is determined by the heading angle δ of each UAV j (t) jointly determine that by optimizing the heading angle of the UAV at time t, the average output signal-to-noise ratio is maximized. Then the trajectory optimization criterion of the UAV in step S3.3 is:

[0040]

[0041] st|δ j,t -δ j,t-Δt |≤δ max (25)

[0042] in, is the optimal heading angle of the jth UAV at time t, δ j,t is the heading angle of the jth UAV at time t, and Δt represents the time interval for UAV position update.

[0043] The beneficial effects of the present invention are:

[0044] The present invention discloses a differentially coded drone relay communication system track optimization method, which can effectively improve the reliability of link transmission of a point-to-point drone relay communication system and reduce the system channel estimation requirements. Compared with a single-transmit and single-receive drone relay transmission scheme, this method uses multi-machine relay, and the link reliability is higher; compared with a beamforming drone relay transmission scheme, this method uses differential coding and does not require accurate channel fading information; compared with a space-time block coding drone relay transmission scheme, this method can reduce the correlation between channels and effectively obtain diversity gain; compared with a collaborative space-time block coding drone relay transmission scheme, the drone track of this method can be adjusted in real time according to the position of the ground node, and the tracking performance is stronger. The system structure of this method is relatively simple, the computational complexity is low, and it is easy to implement.

[0045] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art may be taught from the practice of the present invention. The objectives and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0047] Figure 1 This is a schematic diagram of the differential coding UAV relay communication system of the present invention;

[0048] Figure 2 This is the track model diagram of the UAV of the present invention;

[0049] Figure 3 The UAV track after the ground node coordinate transformation when N=1;

[0050] Figure 4 To relay the flight track map of the UAV;

[0051] Figure 5 This is the effect of path loss factor on the UAV track;

[0052] Figure 6a and Figure 6b They are the influence diagrams of path loss factor on average output signal-to-noise ratio and bit error probability;

[0053] Figure 7 This is the effect of the maximum turning angle on the UAV track;

[0054] Figure 8a and Figure 8b They are the influence diagrams of the maximum turning angle on the average output signal-to-noise ratio and the bit error probability;

[0055] Figure 9a The impact of the number of relay drones on the trajectory when N = 1, 2

[0056] Figure 9b The impact of the number of relay drones on the trajectory when N = 3;

[0057] Fig.10a and 10b These are the effects of the number of relay drones on the average output signal-to-noise ratio and bit error probability. DETAILED DESCRIPTION

[0058] Figure 1 The schematic diagram of the differential coding UAV relay communication system is shown. The system consists of N+2 nodes, including mobile users (MU), N fixed-wing UAVs (UAV1, UAV2...UAVN) and base stations (BS). Assume that the MU and BS nodes cannot communicate directly due to the long distance, and need to build a communication link through the UAV equipped with a relay payload. In order to improve the link transmission reliability of the UAV relay communication system and reduce the system channel estimation requirements, a relay transmission scheme with differential coding of N UAVs is adopted. The MU node uses differential quadrature phase shift keying (DQPSK) to modulate the signal, the UAV node uses amplify and forward (AF) to forward the signal, and the BS node uses the correlation combining method to receive the signal.

[0059] 1. Signal model: In the DQPSK (Differential Quadrature Phase Shift Keying) scheme, the modulator modulates information by the phase difference between two consecutive symbols. The DQPSK signal constellation contains M = 4 symbols, where m represents the DQPSK symbol number, and v m Represents the modulated complex symbol, each symbol is generated by the following formula:

[0060] v m =e j2πm / M ,m∈[0,M-1] (1)

[0061] x (k) Defined as the kth differential encoding symbol, the initial symbol of the differential modulator is specified as x (0) =1, the subsequent symbols are:

[0062] x (k) =v m x (k-1) (2)

[0063] The MU node will differential symbol x (k) The signal is sent to the UAV node. Due to the broadcast characteristics of the signal, both UAV1 and UAV2 will receive this signal. The jth UAV receives the signal containing x (k) The signal It can be expressed as:

[0064]

[0065] Where j = 1, 2...n represents the drone serial number, P M Indicates the transmit power of the MU node, represents the channel fading coefficient from MU to the jth UAV, Represents the additive white Gaussian noise with a mean of 0 and a variance of N0 introduced at the j-th UAV receiving antenna.

[0066] The UAV multiplies the received signal by the corresponding gain factor

[0067]

[0068] Since the channel fading in the system is unknown information, according to the channel model given in the next section, the channel fading Small-scale fading uses long-term averages Instead, formula (4) is rewritten as:

[0069]

[0070] Subsequently, the UAV node transmits a signal to the BS node. The signal received by the BS node from the jth UAV can be expressed as:

[0071]

[0072] in represents the signal from the jth UAV received by the BS, P U It represents the forwarding power of the UAV node. It is assumed here that the forwarding powers of the two UAVs are equal. represents the channel fading coefficient from the jth UAV to the BS, represents the additive Gaussian white noise with mean 0 and variance N0 introduced by the jth relay link at the BS. At the BS, the signal of each relay link is multiplied by the conjugate of the received signal of the previous time slot of the link, and then combined to obtain:

[0073]

[0074] in Represents the output of the BS combiner containing x (k) The signal, w j Represents the optimal merge weight:

[0075]

[0076] When decoding differential codes, the minimum Euclidean distance criterion is usually used. Representative judgment:

[0077]

[0078] Combining equation (7) and equation (9), the combined signal The decoding decision criterion can be expressed as:

[0079]

[0080] 2. Channel model: When studying the trajectory optimization problem of UAV relay communication, it is necessary to comprehensively consider the influence of small-scale and large-scale fading of the channel. Therefore, the channels from MU to UAV node and UAV to BS node are modeled as Rayleigh fading channels including path loss:

[0081]

[0082] In the formula, is the small-scale fading coefficient of the channel from MU to the jth UAV receiving antenna, is the small-scale fading coefficient of the channel between the j-th UAV and the BS node receiving antenna, and both obey a complex Gaussian distribution with a mean of 0 and a variance of 1. represents the channel fading coefficient from the MU node to the jth UAV node, represents the channel fading coefficient from the jth UAV to the BS node, d M,j(t) and d j,B (t) represents the distance from MU to the jth UAV node and the distance from the jth UAV node to BS at time t, respectively, and α represents the path loss factor.

[0083] Assume that the coordinates of the BS node are (x B ,y B ,0), the coordinates of the MU node at time t are (x M (t),y M (t),0), the coordinates of the jth UAV node at time t are (x j (t),y j (t),h j (t)), then the distances from MU to UAV node and from UAV to BS node at time t are:

[0084]

[0085] Assuming that the UAV flight altitude is constant at h and the speed is constant at v, the position coordinates of the UAV at time t can be obtained from its coordinates at time t-Δt using equation (13) [8]:

[0086]

[0087] Among them, δ j (t) is the heading angle of the jth UAV at time t, satisfying δ j (t-Δt)-δ max ≤δ j (t)≤δ j (t-Δt)+δ max , δ max represents the maximum turning angle of the UAV, and Δt represents the time interval for the UAV position update. Substituting equation (13) into equation (12), we get:

[0088]

[0089] For the convenience of expression, d M,j (t), d j,B (t) and Abbreviated as d M,j d j,B , and

[0090] 3. Track optimization method for relay drones: In the system described in this paper, since the MU is moving, when its position coordinates change, the output signal-to-noise ratio of the BS combiner also changes. From formula (7), it can be obtained that after the combiner processes the signal related to the kth symbol, the instantaneous signal-to-noise ratio of the output signal is γ(k) for:

[0091]

[0092]

[0093] Similar to formula (5), the small-scale fading of each channel fading coefficient in formula (16) is replaced by the long-term average value, and formula (15) can be rewritten as:

[0094]

[0095] in:

[0096]

[0097] In the formula represents the average signal-to-noise ratio of the output signal after the combiner processes the correlation signal of the kth differential symbol. Equation (18) shows that the main factor affecting the average output signal-to-noise ratio is the large-scale fading of the channel.

[0098] Assuming that the UAV position is given at time t-Δt, it can be known from equation (14) that the large-scale fading of the channel at time t is determined by the heading angle δ of each UAV j (t) Joint decision, δ j (t) is hereinafter referred to as δ j,t Therefore, the average output signal-to-noise ratio can be maximized by optimizing the heading angle of the UAV at time t:

[0099]

[0100] in, Represents the optimal heading angle of the jth UAV at time t.

[0101] The problem described by formula (19) is an N-dimensional nonlinear optimization problem with boundary constraints, which is very difficult to solve directly. When the coordinate information of each node is known, for The linear accumulation of is δ j,t The univariate function of , and the heading angles of the UAVs are independent of each other, so the above problem can be converted into N one-dimensional nonlinear optimization problems with boundary constraints:

[0102]

[0103] For the optimization problem shown in equation (20), the linear search method can be used to independently optimize the heading angle of each UAV.

[0104] 4. UAV distance constraint method: Figure 2As shown in the figure, the coordinate system is constructed with the direction of the line connecting MU and BS as the x-axis and the direction perpendicular to the line as the y-axis. Assume that the coordinates of MU and BS are (x M ,y M ) and (x B ,y B ). According to the optimization criterion shown in formula (20), there is an ideal position A, whose coordinates are (x A ,y A ), so that the average output signal-to-noise ratio reaches the maximum value within the range of values. Obviously, point A should be located between the line connecting MU and BS. The track of the UAV can be divided into two stages. When far away from point A, the track of the UAV is an arc or straight line tending to point A. The specific shape depends on the initial heading angle and maximum turning angle of the UAV. When near point A, the track of the UAV is a circle formed by circling at the maximum turning angle. Its radius r can be calculated by formula (21), and point A must be on or inside the circle.

[0105]

[0106] Since point A must be located inside or on the circle formed by the drone's hovering, the y-axis flight range of the drone in the hovering state is limited, and its specific range is [y A -2r,y A +2r], changing the coordinates of point A can simultaneously change the UAV track range, thereby achieving UAV distance constraint.

[0107] like Figure 3 As shown in the figure, the coordinates of MU and BS are changed to MU' and BS'. MU' and BS' are virtual points used only for UAV distance constraint, and their coordinates are (x M ,y M +d) and (x B ,y B +d), d is an arbitrary real number. Replacing the original node coordinates in equation (14) with the transformation point coordinates, the ideal position A of the UAV becomes A', and A' is located between the line connecting MU' and BS', and its coordinates are (x A +d,y A +d), the drone's hovering track will change from near point A to near point A', and its flight range becomes [y A +d-2r,y A +d+2r]. Adjust the value of d so that [y A +d-2r,y A +d+2r] and [y A -2r,y A +2r] The value ranges of the two intervals do not overlap, so the distance constraint of the UAV can be achieved.

[0108] Based on the above analysis, a UAV distance constraint method based on coordinate transformation is given below.

[0109] First, according to the number of UAV relays N, the coordinates of the virtual points after MU and BS transformation are calculated. When N = 2ζ, (ζ = 1, 2, ...), the coordinates of the virtual point MUj corresponding to the jth UAV are (x M ,y M +(2j-2ζ-1)d), the coordinates of BSj are (x B ,y B +(2j-2ζ-1)d); when N=2ζ+1, the coordinates of the virtual point MUj corresponding to the jth UAV are (x M ,y M +2(j-ζ-1)d), the coordinates of BSj are (x B ,y B +2(j-ζ-1)d). The coordinates of the virtual point are simply written as and

[0110] Secondly, the coordinates of the virtual point are used to replace the coordinates of MU and BS in equation (14), and the distance between the UAV and the virtual point is obtained as:

[0111]

[0112] where d′ M,j represents the distance from the jth UAV to MUj, d j ' ,B Denotes the distance from the jth UAV to BSj. Use d′ M,j With d j ' ,B Replace d in formula (18) M,j With d j,B , the average output signal-to-noise ratio of the system after coordinate transformation is:

[0113]

[0114] in It is only used for track optimization and has no actual physical meaning.

[0115] Finally, when the UAV is performing trajectory optimization, it uses Replace (20) Then the trajectory optimization criterion of the jth UAV becomes:

[0116]

[0117] st|δ j,t -δ j,t-Δt |≤δ max (25)

[0118] Each UAV performs trajectory optimization according to formula (25) to achieve the distance constraint between UAVs.

[0119] In order to verify the correctness of the proposed scheme, a differential coding UAV relay communication simulation system is built based on Matlab to simulate the UAV trajectory, bit error probability and average output signal-to-noise ratio under given parameters, and the simulation results under different parameters are compared and discussed. The simulation system consists of four nodes: MU, UAV1, UAV2 and BS. Table 1 gives the main technical parameters of the simulation system.

[0120] Table 1 Simulation parameter settings

[0121] Figure 4 The flight trajectory of the relay UAV is given (independent optimization method and exhaustive search method). The horizontal and vertical coordinates represent the two-dimensional rectangular coordinate system, where MU represents the motion trajectory of the MU node, and the solid line UAV1 step With UAV2 step They represent the trajectories of UAV1 and UAV2 obtained by independent optimization methods, and the dotted line UAV1 search With UAV2 search They represent the tracks of UAV1 and UAV2 obtained by exhaustive search method respectively.

[0122] The comparison of flight tracks shows that: 1) the flight tracks of the UAV obtained by the independent optimization method and the exhaustive search method are basically the same, which verifies the correctness of the proposed track optimization method. 2) The tracks of the two UAVs in the figure overlap partially, but the two UAVs are at different times at the overlapping position. In fact, the two UAVs maintain a certain distance throughout the whole process, which verifies the effectiveness of the proposed UAV distance constraint method.

[0123] Figure 5 The influence of path loss factor on the UAV track is given (path loss factor is 2, 2.1, 2.2). α＝2 With UAV2 α＝2 Represents the trajectory when the path loss factor is 2, UAV1 α＝2.1 With UAV2 α＝2.1 Represents the trajectory when the path loss factor is 2.1, UAV1 α＝2.2 With UAV2 α＝2.2 The representative trajectory when the path loss factor is 2.2. The comparison shows that: 1) the proposed method can obtain stable flight trajectory under different path loss factors; 2) the UAV trajectory does not change significantly when the path loss factor increases.

[0124] Figure 6 shows the impact of the path loss factor on the average output signal-to-noise ratio and bit error probability.α＝2 and BER α＝2 They represent the average output signal-to-noise ratio and bit error probability when the path loss factor is 2, SNR α＝2.1 and BER α＝2.1 They represent the average output signal-to-noise ratio and bit error probability when the path loss factor is 2.1, SNR α＝2.2 and BER α＝2.2 They represent the average output signal-to-noise ratio and bit error probability when the path loss factor is 2.2. The curve comparison shows that as the path loss factor increases, the average output signal-to-noise ratio decreases and the bit error probability increases.

[0125] Figure 7 The influence of the maximum turning angle on the UAV track is given. δ＝15° and UAV2 δ＝15° The dotted line represents the track when the maximum turning angle is 15°. δ＝10° and UAV2 δ＝10° The representative trajectory is when the maximum turning angle is 10°. The trajectory comparison shows that: 1) the proposed method can obtain stable flight trajectory under different maximum turning angles; 2) the hovering radius of the UAV decreases as the maximum turning angle increases.

[0126] Figure 8 shows the effect of the maximum turning angle on the average output signal-to-noise ratio and bit error probability. δ＝15° and BER δ＝15° Represents the average output signal-to-noise ratio and bit error probability when the maximum turning angle is 15°, SNR δ＝10° and BER δ＝10° The average output signal-to-noise ratio and bit error probability represent the maximum turning angle of 10°. The curve comparison shows that the fluctuation of system performance decreases with the increase of the maximum turning angle.

[0127] Figure 9 shows the effect of the number of relay UAVs on the trajectory (N = 1, 2, 3). N＝1 The curve represents the trajectory of the UAV when N=1, UAV1 N＝2 and UAV2 N＝2 The curve represents the trajectory of the UAV when N = 2; UAV1 in Figure 9(b) N＝3 、UAV2 N＝3 and UAV3 N＝3 Represents the trajectory of the drone when N=3. For fairness, the forwarding power of the drone when N=2 is 1 / 2 of that when N=1, and the forwarding power of the drone when N=3 is 1 / 3 of that when N=1. Figure 9 shows that: 1) the trajectory optimization method is still correct when the number of relay drones is changed; 2) the drone distance constraint method is still effective when the number of relay drones is changed.

[0128] Figure 10 shows the impact of the number of relay drones on link transmission performance. N＝1 and BER N＝1 Represents the average output signal-to-noise ratio and bit error probability when N=1, SNR N＝2 and BER N＝2 Represents the average output signal-to-noise ratio and bit error probability when N=2, SNR N＝3 and BER N＝3 It represents the average output signal-to-noise ratio and bit error probability when N = 3. The curve comparison shows that as the number of relay drones increases, the average output signal-to-noise ratio of the system increases and the bit error probability decreases.

[0129] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A differentially coded UAV relay communication system track optimization method, characterized in that: The following steps are involved: S1. Establish a differentially coded UAV relay communication system model; S2, establish a trajectory planning model for the UAV relay communication system based on the average output signal-to-noise ratio maximization criterion; S3, realizing the distance constraint of UAV by using the coordinate transformation method; Step S1 specifically includes: S1.1, the system is composed of a ground mobile user node, a ground base station node, and multiple drone relay nodes; S1.2, let the signal be transmitted through a transmitting antenna at a certain power at the source node at time t in a differentially coded manner; S1.3, all drone nodes forward the received signals to the destination node using amplification and forwarding at a certain power; S1.4, the destination node processes the received signal using a correlation combining method and a differential demodulation method; Step S2 specifically includes: S2.1, solve the distance from the source node and the destination node to each drone; S2.2, solve the average output signal-to-noise ratio of the UAV relay communication system at time t; S2.3, solving the optimal heading angle and obtaining the optimal track of the UAV based on the average output signal-to-noise ratio maximization criterion; The system average output signal-to-noise ratio is calculated by the signal related to the kth symbol. Defined as: in, is the long-term average value of the channel fading coefficient from the MU node to the jth UAV node, is the long-term average of the channel fading coefficient from the jth UAV node to the BS node, α represents the path loss factor, and d′ M,j represents the distance from the jth UAV to MUj, d j ' ,B represents the distance from the jth UAV to BSj; Assuming that the UAV position is given at time t-Δt, the large-scale fading of the channel at time t is determined by the heading angle δ of each UAV j (t) jointly determine that by optimizing the heading angle of the UAV at time t, the average output signal-to-noise ratio is maximized. Then the trajectory optimization criterion of the UAV in step S3.3 is: st|d j,t -d j,t-Δt |≤δ max (25) in, is the optimal heading angle of the jth UAV at time t, δ j,t is the heading angle of the jth UAV at time t, and Δt represents the time interval for UAV position update.

2. According to claim 1, a differentially coded UAV relay communication system track optimization method is characterized in that: Step S3 specifically includes: S3.1, solving the virtual node coordinates after the ground node transformation; S3.2, replace the ground node coordinates in S2.1 with the virtual node coordinates; S3.

3. Use the trajectory optimization method after coordinate transformation to obtain the optimal trajectory of the UAV.

3. The method for optimizing the track of a differentially coded UAV relay communication system according to claim 2, characterized in that: In step S1.2, the signal containing x(k) received by the jth UAV Defined as: Where j = 1, 2...n represents the drone serial number, P M Indicates the transmit power of the MU node, represents the channel fading coefficient from MU to the jth UAV, Represents the additive white Gaussian noise with a mean of 0 and a variance of N0 introduced at the j-th UAV receiving antenna.

4. The method for optimizing the track of a differentially coded UAV relay communication system according to claim 3, characterized in that: In step S1.3, the signal received by the BS node from the jth UAV Defined as: in represents the signal from the jth UAV received by the BS, P U represents the forwarding power of the UAV node. Here, the forwarding powers of the two UAVs are equal. represents the channel fading coefficient from the jth UAV to the BS, Represents the additive white Gaussian noise with mean 0 and variance N0 introduced by the j-th relay link at the BS.

5. The method for optimizing the track of a differentially coded UAV relay communication system according to claim 4, characterized in that: In step S1.4, the BS combiner output contains x (k) The signal Defined as: Among them, w j represents the optimal merging weight.

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