Short packet communication method for UAV relay systems based on NOMA technology

Abstract

This disclosure proposes a short packet communication method for UAV relay systems based on NOMA technology. The method includes the following steps: establishing a system model of the UAV relay system, which includes a control terminal, UAVs, and multiple target users; short packet communication transmission between the control terminal, UAVs, and multiple target users based on NOMA technology; and, based on the system model, jointly optimizing the total data packet length, UAV flight position, and UAV power allocation in the short packet communication transmission to obtain the minimum effective packet error rate for the target users. The method proposed in this disclosure can effectively reduce the communication latency of short packet transmission in UAV relay systems and the effective packet error rate at the target users.

Description

Technical Field

[0001] This disclosure relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a short packet communication method for a UAV relay system based on NOMA technology. Background Technology

[0002] Unmanned Aerial Vehicle (UAV) communication has attracted widespread attention due to its high channel gain and flexible deployment. UAVs fly at relatively high altitudes, enabling line-of-sight communication and effectively reducing shadowing effects and signal fading. If there is no direct communication link between two ground nodes, a communication scheme using UAVs as mobile relays can be adopted.

[0003] Ultra-Reliable and Low-Latency Communication (URLLC) is one of the three pillar applications of 5G mobile communication networks. In URLLC, the Shannon formula for unlimited packet length is no longer used because the packet error rate (PER) at the receiver cannot be ignored. Therefore, low-latency transmission of UAV communication systems can be guaranteed through short packet communication within finite packets.

[0004] Non-Orthogonal Multiple Access (NOMA) technology can guarantee fairness among users and scheduling flexibility, thereby improving the system's spectral efficiency and increasing the number of connected devices. To analyze NOMA performance, Successive Interference Cancellation (SIC) is often used to completely eliminate co-channel interference and effectively decode the required signal. The advantages of NOMA technology have led to its widespread application in various wireless communications, such as Physical Layer Security (PLS), full-duplex transmission, and broadcast channels. However, research on the role of NOMA in reducing transmission delay in short-packet communication, and the impact of finite packet length on NOMA performance characteristics, is limited. Therefore, it is necessary to provide a new technical solution to address one or more of the problems existing in the aforementioned solutions.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this disclosure is to provide a short packet communication method for UAV relay systems based on NOMA technology, which can effectively reduce the communication latency of short packet transmission and the effective packet error rate at the target user in the UAV relay system. The method includes the following steps:

[0007] A system model for an unmanned aerial vehicle (UAV) relay system is established, comprising a control terminal, an UAV, and multiple target users; the control terminal, the UAV, and the multiple target users perform short packet communication transmission based on NOMA technology.

[0008] Based on the system model, the total length of data packets, UAV flight position, and UAV power allocation in the short packet communication transmission are jointly optimized to obtain the minimum effective packet error rate for the target user.

[0009] In an exemplary embodiment of this disclosure, in the step of establishing a system model of the unmanned aerial vehicle relay system...

[0010] The control terminal and the multiple target users are located on the ground, and the drone is flying horizontally at an altitude of H above the ground.

[0011] The multiple target users include a high-channel-gain user R1 and a low-channel-gain user R2. The high-channel-gain user R1 is located at the position with the maximum channel gain within the coverage area of ​​the UAV, and the low-channel-gain user R2 is located at the position with the minimum channel gain within the coverage area of ​​the UAV.

[0012] The coordinates of the control terminal are (D0,0), D0=0; the coordinates of the high channel gain user R1 are (D1,0), and the coordinates of the low channel gain user R2 are (D2,0); the amount of data packet from the control terminal to the UAV is L0 bits, the amount of data packet from the UAV to the high channel gain user R1 is L1 bits, and the amount of data packet from the UAV to the low channel gain user R2 is L2 bits, L0=L1+L2;

[0013] The expression for the total length M of the data packets in the short packet communication transmission includes:

[0014] m1+m2+m0≤B·T max =M (1)

[0015] Where M represents the total length of the data packet; T max The total transmission time of the short packet communication is represented by m0; B represents the bandwidth of the system model; m0 represents the data packet length from the control terminal to the UAV; m1 represents the data packet length from the UAV to the high-channel-gain user R1; m2 represents the data packet length from the UAV to the low-channel-gain user R2.

[0016] The transmission channel of the short packet communication transmission follows free-space path loss, and the channel gain h of the transmission channel is... i The expressions include:

[0017]

[0018] Where β0 represents the channel gain per unit distance; H represents the horizontal flight altitude of the UAV; D i Represents the x-coordinate; x i h0 represents the transmission signal from the control terminal; h1 represents the channel gain from the control terminal to the UAV; h2 represents the channel gain from the UAV to the high-channel-gain user R1; and h1 > h2.

[0019] The control terminal, the drone, and the multiple target users are all connected via Loss-of-Sight (LoS) links, and the packet error rate ε of the LoS links is... i The expressions include:

[0020] ε i =Q(f(γ) i ,m i ,L i ),i=0,1,2 (3)

[0021] in, The right-tail function of the standard normal distribution; γ i Indicates signal-to-noise ratio; m i Indicates the data packet length; L i This indicates the amount of information contained in the data packet; V i V represents the dispersion of the transmission channel. i =1-(1+γ) i ) -2 ,i∈{0,1,2}; any of the aforementioned LoS links satisfies ε i ∈[0,0.5].

[0022] In an exemplary embodiment of this disclosure, the expression for the received signal y1 at the high-channel-gain user R1 includes:

[0023]

[0024] Where x1 represents the transmitted signal from the control terminal to the high-channel-gain user R1; x2 represents the transmitted signal from the control terminal to the low-channel-gain user R2; P1 represents the allocated power at the high-channel-gain user R1; P2 represents the allocated power at the low-channel-gain user R2; and n1 represents the normalized noise power at the high-channel-gain user R1.

[0025] The signal-to-interference-plus-noise ratio γ of the transmitted signal x2 transmitted from the control terminal to the low-channel-gain user R2 at the high-channel-gain user R1 is... 21 The expressions include:

[0026]

[0027] The expression for the effective packet error rate of the transmitted signal x1 transmitted by the control terminal to the high-channel-gain user R1 at the high-channel-gain user R1 includes:

[0028]

[0029] in, ε represents the effective packet error rate of the transmitted signal x1 at the high-channel-gain user R1; 21 Indicates with γ 21 The corresponding effective error probability; ε 11 ε1 represents the effective error probability corresponding to the effective packet error rate of the transmitted signal x1 transmitted from the control terminal to the high channel gain user R1; γ1 represents the effective error probability corresponding to γ1; and γ1 represents the signal-to-noise ratio at the high channel gain user R1.

[0030] In an exemplary embodiment of this disclosure, the expression for the received signal y2 at the low-channel user R2 includes:

[0031]

[0032] Where n2 represents the normalized noise power at the low channel gain user R2;

[0033] The signal-to-interference-plus-noise ratio γ of the transmitted signal x2 transmitted from the control terminal to the low-channel-gain user R2 at the low-channel-gain user R2 is... 22 The expressions include:

[0034]

[0035] The expression for the effective packet error rate of the transmitted signal x2 from the control terminal to the low-channel-gain user R2 at the low-channel-gain user R2 includes:

[0036]

[0037] in, γ2 represents the effective packet error rate of the transmitted signal x2 at low-channel-gain user R2; γ2 represents the signal-to-noise ratio at low-channel-gain user R2; m2 represents the length of the data packet from the UAV to low-channel-gain user R2; L2 represents the information content of the data packet at low-channel-gain user R2.

[0038] In an exemplary embodiment of this disclosure, the step of jointly optimizing the total length of data packets, UAV flight position, and UAV power allocation in the short packet communication transmission based on the system model to obtain the minimum effective packet error rate for the target user includes:

[0039] The joint optimization is decomposed into three sub-problems, namely:

[0040] First sub-problem: Given the UAV's flight position and allocated power, optimize the total length of the data packet;

[0041] The second sub-problem is to optimize the flight position of the UAV given the total length of the data packet and the allocated power of the UAV.

[0042] The third sub-problem: Given the total length of the data packet and the flight position of the UAV, optimize the power allocation of the UAV;

[0043] The monotonicity and concavity / convexity of the objective functions in the first subproblem, the second subproblem, and the third subproblem are analyzed respectively.

[0044] Based on the analysis results, the minimum effective packet error rate for the target user is solved using an overall iterative optimization algorithm.

[0045] In an exemplary embodiment of this disclosure, the expression for joint optimization includes:

[0046]

[0047]

[0048] m0, m1, m2 ∈ Z(10b)

[0049] x1≤x≤x2(10c)

[0050] P1m1+P2m2≤P(M-m0)(10d)

[0051] 0≤P1≤P2(10e)

[0052]

[0053] Where st represents the constraint condition, i.e., "constrained by"; m0 represents the data packet length from the control terminal to the UAV; m im1 represents the length of the data packet from the UAV to the high-gain user R1; m2 represents the length of the data packet from the UAV to the low-gain user R2; M represents the total length of the data packet; Z represents the set of positive integers; P represents the maximum transmit power of the UAV; P1 represents the allocated power at the high-gain user R1; P2 represents the allocated power at the low-gain user R2. xi represents the effective packet error rate of the transmitted signal x1 at the high-channel-gain user R1; xi represents the transmitted signal from the control end; x1 represents the transmitted signal from the control end to the high-channel-gain user R1; x2 represents the transmitted signal from the control end to the low-channel-gain user R2; x represents the flight position of the UAV. express Threshold value; Describe the objective function. α = L2 / L0; L2 represents the amount of information in the data packets from the UAV to the low-channel-gain user R2; L0 represents the amount of information in the data packets from the control terminal to the UAV; This represents the effective packet error rate from the control unit to the drone; This represents the effective packet error rate of the transmitted signal x2 transmitted from the control terminal to the low-channel-gain user R2 at the low-channel-gain user R2; min represents minimizing this rate.

[0054] In an exemplary embodiment of this disclosure, the expression for the first subproblem includes:

[0055]

[0056] stm1=m2=M-m0 (11a)

[0057] m0, m1, m2 ∈ Z (10b)

[0058]

[0059] Wherein, the objective function It contains two concave functions.

[0060] In an exemplary embodiment of this disclosure, the expression for the second subproblem includes:

[0061]

[0062] stx1≤x≤x2 (10c)

[0063] P1m1+P2m2≤P(M-m0) (10d)

[0064] Wherein, the objective function It contains two concave functions.

[0065] In one exemplary embodiment of this disclosure, the expression for the third subproblem includes:

[0066]

[0067] stP1m1+P2m2≤P(M-m0) (10d)

[0068] 0≤P1≤P2 (10e)

[0069]

[0070] Wherein, the objective function It contains two concave functions.

[0071] In one exemplary embodiment of this disclosure, the overall iterative optimization algorithm includes an alternating direction multiplier algorithm and an optimal solution algorithm;

[0072] The expression for the optimal solution to the first subproblem includes:

[0073]

[0074] Where m1 = m2 = m; represents the optimal solution to the objective function of the first subproblem; inf represents the infimum;

[0075] The first subproblem is solved iteratively using the alternating direction multiplier algorithm to obtain the optimal total data packet length for the target user when the effective packet error rate is minimized.

[0076] The second subproblem is solved iteratively using the optimal solution algorithm to obtain the optimal UAV flight position for the target user when the effective packet error rate is minimized.

[0077] The optimal solution algorithm is used to iteratively solve the third subproblem to obtain the optimal UAV power allocation for the target user when the effective packet error rate is minimized.

[0078] The technical solution provided in this disclosure may include the following beneficial effects:

[0079] This disclosure proposes a short packet communication method for UAV relay systems based on NOMA technology. This method establishes a system model of the UAV relay system and, based on this system model, jointly optimizes the total data packet length, UAV flight position, and UAV power allocation to obtain the minimum effective packet error rate at the target user. This method can effectively reduce the communication latency of short packet transmission in the UAV relay system and the effective packet error rate at the target user. Attached Figure Description

[0080] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0081] Figure 1 This diagram illustrates the steps of a short packet communication method for an unmanned aerial vehicle (UAV) relay system based on NOMA technology in an exemplary embodiment of this disclosure.

[0082] Figure 2 A schematic diagram illustrating a system model of an unmanned aerial vehicle (UAV) relay system based on NOMA technology in an exemplary embodiment of this disclosure is shown.

[0083] Figure 3 The diagram shows the curves of the functions g(x), g′(x), and g″(x) as a function of x in an exemplary embodiment of this disclosure.

[0084] Figure 4 This diagram shows the grouping error rate as a function of the number of iterations under the multi-parameter joint optimization algorithm in a simulation experiment of an exemplary embodiment of this disclosure.

[0085] Figure 5 The figure shows the grouping error rate as a function of the number of iterations under the alternating direction multiplier algorithm in a simulation experiment of an exemplary embodiment of this disclosure.

[0086] Figure 6 This diagram illustrates the variation of packet error rate at user R2 with packet length in a simulation experiment of an exemplary embodiment of this disclosure.

[0087] Figure 7 This diagram illustrates the variation of packet error rate at low-channel-gain user R2 with total transmit power in a simulation experiment of an exemplary embodiment of this disclosure.

[0088] Figure 8 This diagram illustrates the variation of the packet error rate at low-channel-gain user R2 with the UAV's flight position in a simulation experiment of an exemplary embodiment of this disclosure.

[0089] Figure 9 The diagram illustrates the variation of the packet error rate at low-channel-gain user R2 with the flight altitude of the UAV in a simulation experiment of an exemplary embodiment of this disclosure. Detailed Implementation

[0090] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0091] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0092] This example implementation provides a method, such as Figure 1 As shown, the following steps may be included:

[0093] Step S101: Establish a system model of the UAV relay system, the system model including a control terminal, a UAV, and multiple target users; the control terminal, the UAV, and the multiple target users perform short packet communication transmission based on NOMA technology;

[0094] Step S102: Based on the system model, jointly optimize the total length of data packets, UAV flight position, and UAV power allocation in the short packet communication transmission to obtain the minimum effective packet error rate (PER) for the target user.

[0095] This disclosure proposes a short packet communication method for UAV relay systems based on NOMA technology. This method establishes a system model of the UAV relay system and, based on this system model, jointly optimizes the total data packet length, UAV flight position, and UAV power allocation to obtain the minimum effective packet error rate at the target user. This method can effectively reduce the communication latency of short packet transmission in the UAV relay system and the effective packet error rate at the target user.

[0096] The steps of the method proposed in this example embodiment will be described in more detail below.

[0097] In step S101, Short Packet Transmission (SPT) technology has great potential in improving the transmission latency of UAV communication, while Non-Orthogonal Multiple Access (NOMA) technology can effectively improve spectrum utilization and fairness. Therefore, as... Figure 2 As shown, this embodiment utilizes these two technologies to establish a system model for an unmanned aerial vehicle (UAV) relay system. Figure 2 The system model shown includes a control terminal set up on the ground and two target users, represented by Robot 1 and Robot 2 respectively. The UAV flies horizontally at an altitude H above the ground and acts as a Decode-and-Forward (DF) relay.

[0098] In the system model of this embodiment, robot 1 is a high-channel-gain user R1, and robot 2 is a low-channel-gain user R2. The high-channel-gain user R1 is located at the position with the maximum channel gain within the coverage area of ​​the drone, and the low-channel-gain user R2 is located at the position with the minimum channel gain within the coverage area of ​​the drone.

[0099] Here, the coordinates of the control terminal are (D0,0), D0=0; the coordinates of the high channel gain user R1 are (D1,0), and the coordinates of the low channel gain user R2 are (D2,0); the information content of the data packet from the control terminal to the UAV is L0 bits, the information content of the data packet from the UAV to the high channel gain user R1 is L1 bits, and the information content of the data packet from the UAV to the low channel gain user R2 is L2 bits, L0=L1+L2.

[0100] The entire short packet communication transmission process is divided into two stages. The first stage is from the control terminal to the UAV, and the second stage is from the UAV to high-channel-gain user R1 and low-channel-gain user R2. The UAV uses Time Division Multiple Access (TDMA) technology to send command signals to high-channel-gain user R1 and low-channel-gain user R2. If the entire short packet communication transmission process takes place within T... max If the transmission is completed within seconds, and the bandwidth of the system model is B, then the expression for the total length M of the data packets in this short packet communication transmission is:

[0101] m1+m2+m0≤B·T max =M (1)

[0102] Where M represents the total length of the data packet; T maxThe total transmission time of the short packet communication is represented by m0; B represents the bandwidth of the system model; m0 represents the data packet length from the control terminal to the UAV; m1 represents the data packet length from the UAV to the high-channel-gain user R1; and m2 represents the data packet length from the UAV to the low-channel-gain user R2.

[0103] When a drone flies at a certain altitude, during short packet transmission, there are strong line-of-sight (LoS) links between the drone, the control terminal, and multiple target users. Therefore, the transmission channel for short packet communication follows free-space path loss, and the channel gain h of the transmission channel is... i The expressions include:

[0104]

[0105] Where β0 represents the channel gain per unit distance; H represents the horizontal flight altitude of the UAV; D i Represents the x-coordinate; x i h0 represents the transmission signal from the control terminal; h1 represents the channel gain from the control terminal to the UAV; h2 represents the channel gain from the UAV to the high-channel-gain user R1; and h1 > h2.

[0106] The packet error rate ε of the Loss link i The expression is:

[0107] ε i =Q(f(γ) i ,m i ,L i ),i=0,1,2 (3)

[0108] in, The right-tail function of the standard normal distribution; γ i Indicates signal-to-noise ratio; m i Indicates the data packet length; L i This indicates the amount of information contained in the data packet; V i V represents the dispersion of the transmission channel. i =1-(1+γ) i ) -2 ,i∈{0,1,2}; any of the aforementioned LoS links satisfies ε i ∈[0,0.5].

[0109] Based on this, the effective transmission rate of any LoS link can be obtained as r = (1 - ε i )L i / M, i∈{0,1,2}, the corresponding average time delay can be expressed as τ=L i / r=M / (1-ε iThe latency, i.e., the packet length and packet error rate, are related to the PER (Performance-to-Patient) ratio in the system model. In real-world scenarios, the PER in the system model is (1-ε) i As the latency approaches 1, the latency becomes positively correlated with the data packet length. Therefore, using end packets for communication can effectively reduce the transmission latency of the UAV. Furthermore, given a data packet length, the latency and PER (Percentage Error Rate) show the same trend.

[0110] In the system model of this embodiment, the expression for the received signal y1 at the high-channel-gain user R1 includes:

[0111]

[0112] Where x1 represents the transmitted signal from the control terminal to the high-channel-gain user R1; x2 represents the transmitted signal from the control terminal to the low-channel-gain user R2; P1 represents the allocated power at the high-channel-gain user R1; P2 represents the allocated power at the low-channel-gain user R2; and n1 represents the normalized noise power at the high-channel-gain user R1.

[0113] Since h1 > h2, the interference generated by the transmitted signal x2 can be eliminated by using Successive Interference Cancellation (SIC) at the high-channel-gain user R1. The high-channel-gain user R1 first decodes the transmitted signal x2; at this point, the interference generated by the transmitted signal x1 can be considered as noise. Based on formula (4), it can be known that the signal-to-interference-plus-noise ratio (SINR) γ of the transmitted signal x2 transmitted from the control terminal to the low-channel-gain user R2 at the high-channel-gain user R1 is... 21 The expression is:

[0114]

[0115] Here γ is defined 21 The corresponding effective error probability is ε 21 , ε 21 =Q(f(γ) 21 Therefore, the probability that the transmitted signal x2 is correctly decoded and completely removed at the high-channel-gain user R1 is (1-ε). 21 SPT-based UAV communication cannot guarantee ideal continuous interference cancellation, which differs from traditional infinite packet length communication that achieves ideal SIC. Here, there are two scenarios: successful SIC decoding and SIC decoding failure.

[0116] If SIC decoding is successful, based on formula (4), the SNR of the transmitted signal x1 at the high channel gain user R1 can be expressed as: γ1 = P1h1, then the corresponding effective PER is

[0117] If SIC decoding fails, the probability of this occurring is ε. 21 Since the transmitted signal x2 cannot be completely eliminated, it can be considered that the interference always exists. Therefore, the SINR of the transmitted signal x1 at the high-channel-gain user R1 is γ. 11 = (P1h1) / (P2h1+1), in this case the corresponding PER is ε 11 =Q(f(γ) 11 ,m1,L1)).

[0118] Based on the above analysis, the effective PER of the transmitted signal x1 transmitted by the control terminal to the high-channel-gain user R1 at the high-channel-gain user R1 is expressed as follows:

[0119]

[0120] in, ε represents the effective packet error rate of the transmitted signal x1 at the high-channel-gain user R1; 21 Indicates with γ 21 The corresponding effective error probability; ε 11 ε1 represents the effective error probability corresponding to the effective packet error rate of the transmitted signal x1 transmitted from the control terminal to the high channel gain user R1; γ1 represents the effective error probability corresponding to γ1; and γ1 represents the signal-to-noise ratio at the high channel gain user R1.

[0121] Furthermore, combining with formula (4), the expression for the received signal y2 at low-channel user R2 is:

[0122]

[0123] Where n2 represents the normalized noise power at the low channel gain user R2.

[0124] Here, because h1 > h2, SIC is not applicable to removing the interference caused by the transmitted signal x1 at the low-channel-gain user R2. Analysis shows that the transmitted signal x2 at the low-channel-gain user R2 can be obtained by direct decoding, but the transmitted signal x1 will interfere with this decoding process. Based on formula (7), the SINRγ of the transmitted signal x2 transmitted from the control terminal to the low-channel-gain user R2 at the low-channel-gain user R2 is... 22 The expression is:

[0125]

[0126] Accordingly, the expression for the effective packet error rate of the transmitted signal x2 from the control terminal to the low-channel-gain user R2 at the low-channel-gain user R2 is:

[0127]

[0128] in, γ2 represents the effective packet error rate of the transmitted signal x2 at low-channel-gain user R2; γ2 represents the signal-to-noise ratio at low-channel-gain user R2; m2 represents the length of the data packet from the UAV to low-channel-gain user R2; L2 represents the information content of the data packet at low-channel-gain user R2.

[0129] Because there is only one decoding strategy at the low-channel-gain user R2, the PER at the low-channel-gain user R2 is equivalent to the effective PER, i.e.

[0130] In step S102, the step of jointly optimizing the total length of data packets, the UAV's flight position, and the UAV's power allocation in short packet communication transmission to obtain the minimum effective packet error rate for the target user may include the following sub-steps:

[0131] Sub-step S1021: Decompose the joint optimization into three sub-problems, namely:

[0132] First sub-problem: Given the UAV's flight position and allocated power, optimize the total length of the data packet;

[0133] The second sub-problem is to optimize the flight position of the UAV given the total length of the data packet and the allocated power of the UAV.

[0134] The third sub-problem: Given the total length of the data packet and the flight position of the UAV, optimize the power allocation of the UAV;

[0135] Sub-step S1022: Analyze the monotonicity and concavity / convexity of the objective functions in the first subproblem, the second subproblem, and the third subproblem, respectively;

[0136] Based on the analysis results, the minimum effective packet error rate for the target user is solved using an overall iterative optimization algorithm.

[0137] Here, the overall iterative optimization algorithm includes the Alternating Direction Method of Multipliers (ADMM) algorithm and the optimal solution algorithm.

[0138] In this embodiment, in the system model, the control terminal needs to provide L0-bit signals to R1 and R2, where the finite total packet length of information transmission is M. The control terminal ensures reliable communication by fully utilizing command signals. To minimize the effective PER at R2 under reliability and total power constraints, the joint optimization expression can be described as:

[0139]

[0140]

[0141] m0, m1, m2 ∈ Z (10b)

[0142] x1≤x≤x2 (10c)

[0143] P1m1+P2m2≤P(M-m0) (10d)

[0144] 0≤P1≤P2 (10e)

[0145]

[0146] Where st represents the constraint condition, i.e., "constrained by"; m0 represents the data packet length from the control terminal to the UAV; m i m1 represents the length of the data packet from the UAV to the high-gain user R1; m2 represents the length of the data packet from the UAV to the low-gain user R2; M represents the total length of the data packet; Z represents the set of positive integers; P represents the maximum transmit power of the UAV; P1 represents the allocated power at the high-gain user R1; P2 represents the allocated power at the low-gain user R2. xi represents the effective packet error rate of the transmitted signal x1 at the high-channel-gain user R1; xi represents the transmitted signal from the control end; x1 represents the transmitted signal from the control end to the high-channel-gain user R1; x2 represents the transmitted signal from the control end to the low-channel-gain user R2; x represents the flight position of the UAV. express Threshold value; Describe the objective function. α = L2 / L0; L2 represents the amount of information in the data packets from the UAV to the low-channel-gain user R2; L0 represents the amount of information in the data packets from the control terminal to the UAV; This represents the effective packet error rate from the control unit to the drone; This represents the effective packet error rate of the transmitted signal x2 transmitted from the control terminal to the low-channel-gain user R2 at the low-channel-gain user R2; min represents minimizing this rate.

[0147] Based on the above analysis

[0148] For the first subproblem: Given the UAV's flight position and allocated power, optimize the total packet length to minimize the effective PER for the target user. The expression for the first subproblem can be described as:

[0149]

[0150] stm1=m2=M-m0 (11a)

[0151] m0, m1, m2 ∈ Z (10b)

[0152]

[0153] Wherein, the objective function It contains two concave functions.

[0154] To ensure the reliability constraints of actual communication, the effective PER value of the target user should not exceed 10. -5 To simplify the problem, let's assume the data packet length is m. i The expression for PER at R2 is continuous, i∈{0,1,2}. Solving this problem is difficult because the expression for PER at R2 is complex. Therefore, based on NOMA, the existence of m1=m2=m holds. In this problem, the objective function is... Depend on and Composition, and and These are all functions relating to the output packet length, the drone's flight position, and the drone's power allocation. Therefore, the effective PER can be approximately defined as...

[0155] For ease of writing, let f0(m) = f(γ0,m0,L0) and f2(m) = f(γ2,m2,L2) respectively. To obtain the optimal data packet length, we first analyze the function. Regarding the data packet length m i The monotonicity of, i.e.:

[0156]

[0157] Based on this, the function is obtained through analysis. Regarding the data packet length m i The second derivative:

[0158]

[0159] in, f2(m)(f2′(m)) 2>0, therefore the function can be determined by the magnitude of f2″(m). The nonnegativity of .

[0160] Next, define, V2 = 1 - (1 + γ2) -2 Then f2(m) can be expressed as:

[0161]

[0162] Therefore, we obtain f2(m) with respect to the data packet length m. i The first and second derivatives are as follows:

[0163]

[0164]

[0165] Based on the above analysis, f2″(m) < 0 holds true, therefore f2(m) is a concave function with respect to the data packet length.

[0166] A similar method can be used to modify functions. Solve the problem. Similarly, the function... It is also a strictly concave function with respect to the data packet length m0.

[0167] In summary, we can determine the objective function of the first subproblem. It consists of two concave functions.

[0168] Therefore, the optimal solution to the first subproblem can be expressed as:

[0169]

[0170] Where m1 = m2 = m.

[0171] The ADMM algorithm is then used to iteratively solve the first subproblem to obtain the optimal total data packet length for the target user when the effective packet error rate is minimized.

[0172] It should be noted that the ADMM algorithm can be used to find the optimal value of the packet length; therefore, the Augmented Lagrange Function (ALM) expression is:

[0173]

[0174] Where λ is the Lagrange daily number and ρ represents the quadratic penalty factor. This formula combines the first subproblem with the constraints through the variable λ, and after scaling, it can be rewritten as:

[0175]

[0176] For a more detailed understanding of the ADMM algorithm used here, please refer to Table 1 below.

[0177] Table 1: ADMM Algorithm for Optimizing Total Packet Length

[0178]

[0179] Table 1 shows the specific design steps for optimizing the data packet length. As shown in Table 1, in the k-th iteration, the values ​​of m and λ can be obtained through rows 3-5. Based on this, the round(m) function can round m to the nearest integer. At this point, the optimal m and m0 can be obtained using the bisection method or gradient descent method. λ represents the Lagrange daily number.

[0180] For the second subproblem, given the total packet length and the drone's allocated power, optimize the drone's flight position to minimize the effective PER for the target user. The expression for the second subproblem can be described as:

[0181]

[0182] stx1≤x≤x2 (10c)

[0183] P1m1+P2m2≤P(M-m0) (10d)

[0184] Wherein, the objective function It contains two concave functions.

[0185] Here, similar to the previous definition method, the effective PER at R2 can be expressed as Analysis shows that... It is not a strictly convex function of the drone's flight position x. To solve this problem, within the drone's flight range, we prove, through numerical calculation, that there exists a unique optimal x that minimizes the effective PER at R2.

[0186] For ease of calculation, define function g(x) and The monotonicity is consistent. Let f0(x) = f(γ0(x), m0, L0), f2(x) = f(γ2(x), m2, L2), where γ0(x) and γ2(x) are respectively expressed as γ0(x) = P c β0 / (H 2 +x 2 ), γ2(x)=P2β0 / (P1β0+H 2 +(D2-x) 2Based on the above analysis, in order to obtain the optimal solution of the function g(x), we first analyze the monotonicity of the function g(x), and the expression of its first derivative is:

[0187]

[0188] Based on this, the second derivative of function g(x) with respect to x is obtained as follows:

[0189]

[0190] in, The first and second derivatives with respect to x are as follows:

[0191]

[0192]

[0193] Based on formulas (2) and (8), and It can be derived as follows:

[0194]

[0195]

[0196] and,

[0197]

[0198] In the formula, Z2 = D2 - x. Based on a similar derivation process, the function is also proved to be... The monotonicity and convexity of the function are simplified here. The optimized derivation process.

[0199] In summary, we can see that the objective function of the second subproblem is... It contains two concave functions.

[0200] Subsequently, the second subproblem was solved iteratively using the optimal solution algorithm to obtain the optimal UAV flight position for the target user when the effective packet error rate is minimized.

[0201] Please refer to Table 2 below.

[0202] Table 2: Optimal solution algorithm for optimizing UAV flight position and UAV power allocation

[0203]

[0204]

[0205] It should be noted that, given the flight area of ​​the UAV, g(x) is a non-convex function of x. Correspondingly, the graphs of functions g(x), g′(x), and g″(x) are as follows: Figure 3 As shown. According to Figure 3 It can be seen that g(x) is a quasi-convex function with respect to x. Specifically, when 20 < x < x * When x < 0, the first derivative function g′(x) < 0; when x < 0, the first derivative function g′(x) < 0. * When x < 180, the first derivative function g′(x) > 0. Therefore, given the total data packet length and UAV power allocation, there exists a unique optimal UAV flight position that maximizes the effective PER at R2. The optimal position x of the objective function can be obtained using the algorithm shown in Table 2. opt The search range for the UAV's flight position under this algorithm is [x1, x2], and the corresponding maximum number of searches is (x2-x1) / κ, where κ is the algorithm's precision.

[0206] For the third subproblem, given the total packet length and the drone's flight position, optimize the drone power allocation to minimize the effective PER for the target user. The expression for the third subproblem can be described as:

[0207]

[0208] stP1m1+P2m2≤P(M-m0) (10d)

[0209] 0≤P1≤P2 (10e)

[0210]

[0211] Wherein, the objective function It contains two concave functions.

[0212] To address the effective PER minimization problem, while satisfying the total power constraint, the allocated power at R2 should be increased as much as possible to reduce the corresponding PER. Under NOMA-based technology, the packet length satisfies...

[0213] To analyze the impact of UAV power constraints on the effective PER, we prove by contradiction that condition (10d) yields the optimal effective PER at R2 when the inequality is satisfied. Specifically,

[0214] First, assume the optimal power distribution at R1 and R2 is... and And the power meets The corresponding PER is and Strictly less than the maximum Right now

[0215] Based on formula (8), the SINR at R2 can be expressed as:

[0216]

[0217] Based on this, variables are introduced. Analysis shows that Ω > 1, therefore:

[0218]

[0219] In the formula, P1 * , Meet the conditions The corresponding SINR at this time is:

[0220]

[0221] Based on formula and inequality This holds true. It can be obtained that PER is a monotonically decreasing function with respect to SINR, inherently... This is true, but through analysis, it contradicts the known conditions. Therefore, in order to minimize the effective PER at R2, the condition (10d) can be expressed as P1+P2=P.

[0222] To reduce the complexity of the search algorithm, we prove by contradiction that condition (10d) is equal when the UAV power allocation is optimal. Let the optimal power at R1 be... The corresponding PER is Assume inequalities Therefore, the valid PER at R1 can be rewritten as:

[0223]

[0224] Combining conditions (10d) and have Established.

[0225] Based on formula (3) and γ1=P1h1, define f1(γ)=f(γ1,m1,L1), then the power at R2 can be expressed as P2=(E tot / m)-P1. Based on the above analysis, the lower limit of P1 is:

[0226]

[0227] Based on this, there are Therefore, the effective PER at R1 can be expressed as:

[0228]

[0229] in, It can be obtained from the formula for the lower limit of formula P1. Because the function Since it is continuous, there exists a power value. Make Established. Analysis shows that... It is a monotonically decreasing function of P1, therefore the inequality is correct. If this holds true, then the analysis contradicts the known conditions. Therefore, to obtain the minimum PER, condition (9g) can be expressed as...

[0230] Based on the above analysis, the inequality P1 ≤ P / 2 holds. To ensure that ε2 and ε 21 It has practical significance; the inequalities log2(1+γ2)≥L2 / m and log2(1+γ 21 If L ≥ L² / m holds, then we can conclude that:

[0231]

[0232]

[0233] Because the channel gain satisfies h1 > h2, therefore P 1f <P 1s This holds true. Based on the above analysis, the lower bound of P1 can be expressed as: Therefore, the constraints of the power allocation optimization problem can be rewritten as {P1,P2|P 1min ≤P1≤P 1max Given the data transmission packet length and UAV flight position, there exists a unique optimal power that maximizes the effective power output at the target user location. The optimal power P1 is obtained by using the algorithm shown in Table 2. opt The power range under this algorithm is [P]. 1min ,P 1max The corresponding maximum number of searches is (P) 1max -P 1min ) / κ, where κ is the algorithm precision.

[0234] Then, the third subproblem is solved iteratively using the optimal solution algorithm to obtain the optimal UAV power allocation for the target user when the effective packet error rate is minimized.

[0235] In this embodiment, the monotonicity and concavity / convexity of the objective function in the optimization problems of total data packet length, UAV flight position, and UAV power allocation are studied. Then, a global iterative optimization algorithm is used to minimize the effective PER at the target user by jointly optimizing the first, second, and third subproblems. The specific design of the global iterative optimization algorithm is shown in Table 3 below.

[0236] Table 3: Overall Iterative Optimization Algorithm

[0237]

[0238] As can be seen, in any i iterations, the effective PER is All are non-decreasing functions. The convergence proof of the algorithm proposed in this embodiment is as follows:

[0239]

[0240] Among them, conditions (a), (b), and (c) are in m (i+1) x (i+1) and P1 (i+1) The optimal solutions for the first, second, and third subproblems are taken as equal values. The maximum number of iterations for the algorithm proposed in this embodiment is i. max Because the time complexity of the ADMM algorithm is O(n log n). The time complexity of the optimal solution algorithm is Therefore, the complexity of the algorithm shown in Table 3 is:

[0241] Although the proposed iterative algorithm cannot guarantee obtaining the global optimal solution of an effective PER, simulation results show that the algorithm can achieve the same system performance as the exhaustive method.

[0242] To verify the effectiveness of the method proposed in this embodiment, the following simulation experiments were conducted:

[0243] Here, the performance of the proposed algorithm is analyzed through simulation experiments. Specifically, by jointly optimizing the total data packet length, UAV flight position, and UAV power allocation, the effective PER for the target user is minimized. Furthermore, the effectiveness of orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques is compared when transmitting data packets with limited lengths.

[0244] The specific simulation experiment parameters are set as follows:

[0245] The UAV flies at an altitude of H = 120m, with a system bandwidth of B = 5MHz, β0 = 50dB, and P... c=3W. The coordinates of R1 and R2 are (180m,0) and (200m,0) respectively, and the corresponding data packet sizes are L0 = 200 bits, L1 = 80 bits, and L2 = 120 bits respectively. To ensure the reliability of data transmission, the maximum PER is set to ε. max =10 -5 System transmission time T max =36μs, thus the overall packet length is M=BT max =180.

[0246] When analyzing the convergence of the overall iterative optimization algorithm, such as Figure 4 The figure shows the relationship between PER and the number of iterations at different flight altitudes. It can be seen that the overall accuracy of the iterative optimization algorithm is Φ = 10. -3 The maximum number of iterations is i max The algorithm is in case i > i max or The algorithm converges quickly. As shown in Table 3, the algorithm can quickly reach stability, and the number of convergences is less than 6 at all flight altitudes.

[0247] Therefore, the overall iterative optimization algorithm proposed in this disclosure has good convergence and lower complexity. Furthermore, the flight altitude of the UAV has a significant impact on the effective PER at R2. Specifically, as the flight altitude increases, the communication quality between the UAV and the target user deteriorates, thus increasing the PER at that point.

[0248] When analyzing the convergence of the ADMM algorithm, such as Figure 5 The figure shows the relationship between effective PER and the number of iterations under different UAV flight positions and penalty factors, where the UAV flight altitude is H = 120m, the algorithm accuracy is 10⁻³, and the maximum number of iterations is k. max When k > k max or The ADMM algorithm converges at that time. Figure 5It can be seen that different parameter choices result in different iteration counts. The value of the penalty factor has a significant impact on the effective PER; when the penalty factor is small, the ADMM algorithm requires multiple iterations to converge. In practical applications, the optimal penalty factor can be obtained through exhaustive search. Furthermore, changes in the UAV's flight position also affect system performance; that is, the UAV's flight position affects the channel gain between the UAV and the controller and R2, thus affecting the communication rate. Specifically, when the UAV is closer to the controller, the channel capacity between the UAV and the controller increases, while the channel capacity between the UAV and R2 decreases. Conversely, when the UAV is closer to R2, the communication capacity between the UAV and the controller decreases, while the communication capacity between the UAV and R2 increases. Therefore, choosing a suitable UAV flight position is crucial for reducing the effective PER in a UAV relay communication system.

[0249] like Figure 6 As shown, the effective PER at R2 varies with the total packet length under different system parameters and schemes. It can be seen that, given the UAV's flight position and power allocation, there exists a unique optimal data transmission packet length that optimizes the effective PER at R2, consistent with previous analysis. Furthermore, the UAV's flight position and power allocation are crucial to the PER at R2; specifically, a higher allocated power at R2 results in a higher SINR, thus reducing the effective PER at that point. Compared to the OMA scheme, the NOMA scheme exhibits a superior effective PER, meaning the proposed scheme can achieve the same system performance as the baseline scheme with a shorter packet length. Therefore, in SPT-based UAV relay communication, NOMA technology has a significant advantage in reducing communication latency.

[0250] like Figure 7 As shown, the effective PER at R2 varies with the total transmit power under different system parameters and schemes. It can be seen that, given the UAV's flight position and the total length of the data transmission packet, as the total transmit power increases, the allocated power at R2 increases, thus reducing the effective PER at that point, which is consistent with the previous analysis. Furthermore, within a certain data packet length range, the longer the data packet, the higher the data transmission rate, the better the communication quality, and the smaller the PER. Therefore, the impact of data transmission packet length on system performance cannot be ignored. Based on this, changes in the UAV's flight position will lead to changes in the effective PER at R2. That is, choosing an appropriate flight position and data transmission packet length is crucial for reducing the effective PER at the target user and improving system performance. Under the OMA scheme, the UAV can reduce the effective PER at R2 by allocating more power, but the overall performance is still lower than the NOMA scheme proposed in this chapter. That is, to achieve the same system performance, the latter requires less power, thus having better transmission performance.

[0251] like Figure 8 As shown, the effective PER at R2 varies with flight position under different system parameters and schemes. It can be seen that for a given total data packet length and UAV power allocation, there exists a unique optimal UAV flight position that optimizes the effective PER at R2, consistent with previous analysis. Furthermore, within the feasible region of transmit power, both the optimal flight position and the corresponding effective PER decrease with increasing power. Specifically, as transmit power increases, the distance required between the UAV and the controller to achieve the same channel capacity becomes shorter, thus reducing the optimal flight position. In addition, higher power results in better communication quality, leading to a lower effective PER at the target user, further demonstrating the impact of power on optimization design. Compared to the baseline scheme under the same settings, the NOMA scheme proposed in this chapter consistently outperforms the OMA scheme, demonstrating that NOMA effectively reduces the effective PER at the target user, thereby improving the system performance of UAV-assisted relay communication.

[0252] like Figure 9 As shown, the effective PER at R2 varies with the UAV's flight altitude under different algorithms and schemes. Specifically, as the UAV's flight altitude increases from 95m to 120m, the effective PER at the target user monotonically increases. This indicates that as the flight altitude increases, the communication quality between the UAV and R2 deteriorates, leading to an increase in the system PER, consistent with previous analysis. Furthermore, although the algorithm proposed in this disclosure differs from the exhaustive method in its basic principles, it can achieve approximate results in optimizing data transmission packet length, UAV flight position, and power allocation. Moreover, the proposed iterative optimization algorithm has lower complexity and requires fewer iterations. In addition, compared to the baseline scheme, the proposed NOMA scheme achieves better system performance and effectively solves the PER optimization problem at the target user. Specifically, applying NOMA technology to short packet communication can achieve a lower PER, thus it can be widely applied in various UAV communication scenarios based on SPT.

[0253] In summary, this disclosure introduces SPT (Simultaneous Transmission Platform) into a UAV-assisted relay system based on NOMA (Normally Omnidirectional Multi-mode) transmission to address the UAV transmission latency problem. Under reliability and total power constraints, the effective PER (Performance Ratio) at the target user is optimized by jointly optimizing the total data packet length, UAV flight position, and UAV power allocation. To solve this complex problem, the PER minimization problem is first divided into three sub-problems, which are solved one by one. Based on this, the optimal solutions to the corresponding sub-problems are obtained using the ADMM (Advanced Dynamic Model) algorithm and an optimal solution algorithm. Finally, an overall iterative optimization algorithm is proposed to obtain the optimal solution of the objective function by iteratively optimizing the three sub-problems. Simulation results show that the proposed iterative algorithm has good convergence. Furthermore, compared with the OMA (Normally Omnidirectional Multi-mode) scheme, the proposed NOMA scheme can effectively reduce the communication latency of SPT and improve the system performance at R2.

[0254] It should be noted that although several units of the system for executing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Some or all of the units can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0255] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A short packet communication method for an unmanned aerial vehicle (UAV) relay system based on NOMA technology, characterized in that, Includes the following steps: A system model for an unmanned aerial vehicle (UAV) relay system is established, comprising a control terminal, an UAV, and multiple target users; the control terminal, the UAV, and the multiple target users perform short packet communication transmission based on NOMA technology. Based on the system model, the total length of data packets, UAV flight position, and UAV power allocation in the short packet communication transmission are jointly optimized to obtain the minimum effective packet error rate for the target user. This includes decomposing the joint optimization into three sub-problems: First sub-problem: Given the UAV's flight position and allocated power, optimize the total length of the data packet; The second sub-problem is to optimize the flight position of the UAV given the total length of the data packet and the allocated power of the UAV. The third sub-problem: Given the total length of the data packet and the flight position of the UAV, optimize the power allocation of the UAV; The monotonicity and concavity / convexity of the objective functions in the first subproblem, the second subproblem, and the third subproblem are analyzed respectively. Based on the analysis results, the minimum effective packet error rate for the target user is solved using an overall iterative optimization algorithm; The expression for the joint optimization includes: （10） (10a) (10b) (10c) (10d) (10e) (10f) Where st represents the constraint condition, that is, "constrained by"; Indicates the length of the data packet from the control unit to the drone; Indicates the length of the gain user data packet; This indicates the length of the data packet from the drone to the high-channel-gain user R1; This indicates the length of the data packet from the drone to the low-channel-gain user R2; M Indicates the total length of the data packet; Z Represents the set of positive integers; P This indicates the maximum transmit power of the drone; This represents the allocated power at user R1 with high channel gain; This represents the allocated power at R2 for the low-channel-gain user; This represents the transmitted signal at user R1 with high channel gain. Effective packet error rate at high channel gain user R1; This indicates the transmitted signal from the control terminal; This indicates the transmitted signal from the control terminal to the high-channel-gain user R1; This indicates the transmitted signal from the control terminal to the low-channel-gain user R2; Indicates the drone's flight position; express Threshold value; Describe the objective function. , ; This represents the amount of information in the data packets from the UAV to the low-channel-gain user R2; This indicates the amount of information in the data packets from the control unit to the drone; This represents the effective packet error rate from the control unit to the drone; This represents the transmitted signal from the control terminal to the low-channel-gain user R2. The effective packet error rate at user R2 with low channel gain; min means minimizing.

2. The short packet communication method for a UAV relay system based on NOMA technology according to claim 1, characterized in that, In the step of establishing the system model of the UAV relay system, The control terminal and multiple target users are located on the ground, and the drone's horizontal flight altitude above the ground is [missing information]. H； The multiple target users include a high-channel-gain user R1 and a low-channel-gain user R2. The high-channel-gain user R1 is located at the position with the maximum channel gain within the coverage area of ​​the UAV, and the low-channel-gain user R2 is located at the position with the minimum channel gain within the coverage area of ​​the UAV. The coordinates of the control terminal are: , The coordinates of the high-channel-gain user R1 are: The coordinates of the low-channel-gain user R2 are: The amount of information in the data packet from the control terminal to the drone is: The information content of the data packet from the UAV to the high-channel-gain user R1 is bits. The information content of the data packet from the UAV to the low-channel-gain user R2 is bits. Bit, ; The total length of the data packet in the short packet communication transmission M The expressions include: （1） in, M Indicates the total length of the data packet; Indicates the total transmission time of the short packet communication; B This represents the bandwidth of the system model; Indicates the length of the data packet from the control unit to the drone; This indicates the length of the data packet from the drone to the high-channel-gain user R1; This indicates the length of the data packet from the drone to the low-channel-gain user R2; The transmission channel of the short packet communication transmission follows free-space path loss, and the channel gain of the transmission channel... The expressions include: （2） in, Indicates the channel gain per unit distance; H Indicates the horizontal flight altitude of the drone; Represents the x-axis; This indicates the transmitted signal from the control terminal; This represents the channel gain from the control unit to the UAV; This represents the channel gain from the drone to the high-channel-gain user R1. This represents the channel gain from the drone to the low-channel-gain user R2, and ; The control terminal, the drone, and the multiple target users are all connected via Loss-of-Sight (LoS) links. The packet error rate of the LoS links is... The expressions include: （3） in, The right-tail function of the standard normal distribution; Indicates the signal-to-noise ratio; Indicates the length of the data packet; This indicates the amount of information contained in the data packet; , Indicates the dispersion of the transmission channel. Any one of the aforementioned LoS links satisfies .

3. The short packet communication method for UAV relay systems based on NOMA technology according to claim 2, characterized in that, The received signal at the high channel gain user R1 The expressions include: （4） in, This indicates the transmitted signal from the control terminal to the high-channel-gain user R1; This indicates the transmitted signal from the control terminal to the low-channel-gain user R2; This represents the allocated power at user R1 with high channel gain; This represents the allocated power at R2 for the low-channel-gain user; This represents the normalized noise power at user R1 with high channel gain. The transmission signal transmitted by the control terminal to the low-channel-gain user R2 Signal-to-interference-plus-noise ratio at the high channel gain user R1 The expressions include: （5） The transmission signal transmitted by the control terminal to the high-channel-gain user R1 The expression for the effective packet error rate at the high-channel-gain user R1 includes: （6） in, This represents the transmitted signal at user R1 with high channel gain. Effective packet error rate at high channel gain user R1; Indicates and The corresponding effective error probability; This represents the transmitted signal from the control terminal to the high-channel-gain user R1. The effective error probability corresponding to the effective packet error rate at the high channel gain user R1; express The corresponding effective error probability; This represents the signal-to-noise ratio at user R1 with high channel gain.

4. The short packet communication method for an unmanned aerial vehicle (UAV) relay system based on NOMA technology according to claim 3, characterized in that, The received signal at the low channel gain user R2 The expressions include: （7） in, This represents the normalized noise power at user R2 with low channel gain. The transmission signal transmitted by the control terminal to the low-channel-gain user R2 Signal-to-interference-plus-noise ratio at the low-channel-gain user R2 The expressions include: （8） The control terminal transmits a signal to the low-channel-gain user R2. The expression for the effective packet error rate at the low-channel-gain user R2 includes: （9） in, This represents the transmitted signal at user R2 with low channel gain. Effective packet error rate at low channel gain user R2; This represents the signal-to-noise ratio at R2 for the low-channel-gain user. This indicates the length of the data packet from the drone to the low-channel-gain user R2; This represents the amount of information in the data packets of low-channel-gain user R2.

5. The short packet communication method for an unmanned aerial vehicle (UAV) relay system based on NOMA technology according to claim 1, characterized in that, The expression for the first subproblem includes: （11） （11a） （10b） （10f） Wherein, the objective function It contains two concave functions.

6. The short packet communication method for an unmanned aerial vehicle (UAV) relay system based on NOMA technology according to claim 1, characterized in that, The expression for the second subproblem includes: （12） (10c) (10d) Wherein, the objective function It contains two concave functions.

7. The short packet communication method for an unmanned aerial vehicle (UAV) relay system based on NOMA technology according to claim 1, characterized in that, The expression for the third subproblem includes: （13） （10d） (10e) (10f) Wherein, the objective function It contains two concave functions.

8. The short packet communication method for an unmanned aerial vehicle (UAV) relay system based on NOMA technology according to claim 5, characterized in that, The overall iterative optimization algorithm includes the alternating direction multiplier algorithm and the optimal solution algorithm; The expression for the optimal solution to the first subproblem includes: (14) in, ; This represents the optimal solution to the objective function of the first subproblem; Indicates the infimum; The first subproblem is solved iteratively using the alternating direction multiplier algorithm to obtain the optimal total data packet length for the target user when the effective packet error rate is minimized. The second subproblem is solved iteratively using the optimal solution algorithm to obtain the optimal UAV flight position for the target user when the effective packet error rate is minimized. The optimal solution algorithm is used to iteratively solve the third subproblem to obtain the optimal UAV power allocation for the target user when the effective packet error rate is minimized.

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