Method for minimizing transmission power of RSMA-based UAV communication system under jitter condition

Abstract

The application claims a kind of unmanned aerial vehicle (UAV) communication system transmission power minimization method based on rate splitting multiple access (RSMA) under jitter condition, belongs to the field of RSMA network power control, under the condition of minimum rate constraint of each user, user's common rate constraint, UAV hovering area constraint, the transmission power of UAV communication system based on RSMA under jitter condition is minimized, its innovation lies in, RSMA protocol is combined with considering the UAV communication system under jitter condition.This application uses Successive Convex Approximation (SCA), semi-positive definite relaxation technique and S-procedure and one-dimensional search method to obtain suboptimal solution, the method provided by the application reduces the transmission power compared with other UAV communication systems under jitter condition without using RSMA protocol, has better practicability and feasibility.

Description

Technical Field

[0001] This invention relates to the field of RSMA network power control technology, and specifically to a method for minimizing the transmit power of an RSMA-based UAV communication system under jitter conditions. Background Technology

[0002] Drones are a key technology for next-generation (beyond 5th and 6th generation) wireless networks and will play a crucial role in strengthening the Industry 4.0 revolution. Controlled mobility, ease of implementation, low cost, user-friendly operation, lightweight design, and strong line-of-sight channels are factors contributing to their popularity. One of the most common applications of drones is as aerial base stations, offering higher spectral efficiency and better connectivity, and they are potential candidates for the "airborne connectivity" required for B5G (Beyond-5G) and 6G networks. Drone base stations differ from traditional base stations in that, due to the aforementioned characteristics, they can deploy the required network in specific locations. This enables ground users to obtain high-quality wireless links, poor degradation, high capacity, and low interference. Compared to NOMA and SDMA technologies, RSMA technology can improve downlink communication and service quality performance under both perfect and imperfect transmitter channel state information. Furthermore, compared to NOMA, which requires multiple consecutive interference cancellations, RSMA only requires one consecutive interference cancellation, significantly reducing receiver complexity. Numerous studies have demonstrated that RSMA outperforms other multiple access technologies in wireless network performance. However, existing RSMA-based UAV communication systems all assume that the air-to-ground or enemy-to-air link has perfect channel state information, but the UAV platform is susceptible to the effects of airflow and fuselage vibration, which introduces non-negligible channel estimation errors.

[0003] Currently, research on existing resource allocation methods in UAV communication networks reveals two main problems that hinder their practicality. First, RSMA-based UAV communication systems do not consider the impact of UAV jitter on the channel. For example, Xin Liu et al. published an article titled "Downlink Energy Efficiency Maximization for RSMA-UAV Assisted Communications" in *IEEE Wireless Communications Letters, 2024, 13(1):98-102.*, which, while using the RSMA protocol, did not consider the impact of UAV jitter during aerial operations. Second, existing research on UAV communication under jitter conditions does not apply RSMA. For instance, Huici Wu et al. published an article titled "Energy-Efficient and Secure Air-to-Ground Communication With Jittering UAV" in *IEEE Transactions on Vehicular Technology, 2020, 69(4):3954-3967*, which, while considering UAV jitter, did not utilize RSMA, an advanced multiple access technology.

[0004] Therefore, for UAV base stations serving multiple ground users and without considering UAV jitter, researching a method for minimizing the transmit power of a UAV communication system based on RSMA under jitter conditions has significant practical application value and importance. Since the problem of minimizing the transmit power of a UAV communication system based on RSMA under jitter conditions is a non-convex optimization problem involving an infinite number of possibilities, designing a low-complexity resource allocation method for this problem is a technical challenge. Summary of the Invention

[0005] This invention aims to solve the problems of the prior art. It proposes a method for minimizing the transmit power of a UAV communication system based on RSMA under jitter conditions. The technical solution of this invention is as follows:

[0006] A method for minimizing transmit power in a UAV communication system based on RSMA under jitter conditions, comprising the following steps:

[0007] Step 1) Establish a model for minimizing the transmit power of a UAV communication system based on RSMA under jitter conditions;

[0008] Step 2) Rewrite the problem of minimizing the transmit power of the RSMA-based UAV communication system under jitter conditions into a bi-level programming form;

[0009] Step 3) Initialize the drone's position And the number of iterations t = 0;

[0010] Step 4) Calculate the minimum transmit power in the t-th iteration. The minimum transmit power is solved using an inner-layer optimization algorithm;

[0011] Step 5) Solve using a one-dimensional search method The minimum transmit power for each given UAV location is solved by the inner optimization algorithm;

[0012] Step 6) Solve using a one-dimensional search method The minimum transmit power for each given UAV location is solved by the inner optimization algorithm;

[0013] Step 7) Calculate using inner layer optimization algorithm

[0014] Step 8) If the absolute value of the difference between the current transmit power and the previous transmit power is not greater than the given decision threshold, then the transmit power minimization algorithm is considered to have converged, the minimum transmit power is given, and the method ends; if the absolute value of the difference between the updated transmit power and the previous transmit power is greater than the decision threshold, then the newly calculated transmit power value is saved as the transmit power value at this time, and the process returns to step 4) until the transmit power meets the convergence condition, and the minimum transmit power of the UAV is given.

[0015] Furthermore, the transmit power minimization model (P1) of the RSMA-based UAV communication system under jitter conditions in step 1) is as follows:

[0016]

[0017] C5:x min ≤x U ≤x max y min ≤y U ≤y max

[0018] Where, p i Let be the beamforming vector of the private stream sent by the UAV to the i-th user. Let x represent the noise of the channel from the UAV to the m-th user. min and x max These represent the upper and lower bounds of the UAV's flight area along the x-axis, respectively, and the y-axis represents the lower bound. min and y max Let M represent the upper and lower bounds of the drone's flight area along the y-axis, respectively. The number of users is M. h is the channel vector of the line-of-sight link from the UAV to user m, which is unaffected by UAV jitter. m,N Let m be the expected channel gain of the non-line-of-sight link from the UAV to the m-th user. with h m,N All about UAV location (x U ,y U The function p c p is the beamforming vector of the UAV common flow. m This is the beamforming vector for the private stream sent by the UAV to user m. The uncertainty in the azimuth angle of the line-of-sight link from the drone to user m caused by drone jitter, Δθ m Uncertainty in the elevation angle of the line-of-sight link from the drone to user m due to drone jitter; Ω m This represents the set of uncertainties from the UAV to the m-th user channel, including all possible departure angles; a represents a set of users; m It is the channel vector of the line-of-sight link from the UAV to user m, which is affected by the uncertainty of the azimuth angle, b m It is the channel vector of the line-of-sight link from the UAV to user m, which is affected by the uncertainty of the elevation angle, c m R is the common rate of user m. m,tot,min β is the minimum rate that user m needs to achieve. m It is the maximum value of the link jitter between the drone and user m; x min ≤x U ≤x max ,y min ≤y U ≤y max This indicates that the UAV's flight area is restricted; the optimization variable of problem (P1) is the two-dimensional position (x) of the UAV. U ,y U The objective function (P1) represents the transmit power of the UAV, with constraints C1 indicating that the achievable common rate for each user must be greater than the sum of the common rates allocated to all users, C2 representing the QoS constraint for each user, C3 representing the non-negativity constraint for the common rate for each user, C4 representing the maximum jitter of the link from the UAV to user m, and C5 representing the UAV's flight range constraint. The optimization problem (P1) is solved using continuous convex approximation, semidefinite programming, S-programming, and one-dimensional search.

[0019] Furthermore, in step 2), the problem (P1) is transformed into a two-level programming problem (P2), where the outer variable is the two-dimensional position (x) of the UAV. U ,y U The inner variable is p. c ,{pm ,c m The problem (P2) is represented as follows:

[0020]

[0021] Furthermore, in step 3), the position of the drone is initialized. And the number of iterations t = 0.

[0022] Furthermore, in step 4), the minimum transmit power in the t-th iteration is calculated. The minimum transmit power is solved using an inner-layer optimization algorithm, which is described in detail below:

[0023]

[0024] In constraints C1 and C2

[0025]

[0026] Where {K m |m=1,...,M} represents the Rice factor from the UAV to different user channels, A L and A N These are the path loss factors for line-of-sight and non-line-of-sight channels, α and α, respectively. L and α N These are the path loss exponents for the line-of-sight channel and the non-line-of-sight channel, respectively. m This represents the distance between the drone and the user m. and These are the estimated azimuth and elevation angles of the departure angle between the uniform linear array at the base station and the m-th user, respectively. U It is the antenna spacing; λ c λ is the wavelength of the carrier center frequency, and N is the number of transmitting antennas; h m,N Desired channel gain for the non-line-of-sight link from UAV to the m-th user

[0027] Since the problem described above is non-convex and contains an infinite number of inequality constraints, we first introduce a set of auxiliary variables. And make Problem (P3) is equivalent to problem (P4).

[0028]

[0029] C14:rank(Q c ) = 1

[0030]

[0031] Q c Q represents the product of the beamforming vector of the common flow and its conjugate transpose. m Let represent the product of the beamforming vector of the private stream of the m-th user and its conjugate transpose. a m ,b m ,g m ,j m Both represent auxiliary variables, using This represents the set of the aforementioned auxiliary variables.

[0032] Since problem (P4) is still nonconvex, the exponential terms of constraints C2, C3, C6, and C7 are approximated by continuous convexity at the given point. First-order Taylor expansion, In the k-th iteration The value of is determined, and the infinite number of inequality constraints are transformed into linear matrix inequalities using the S-program. At the same time, the rank-one constraints of C14 and C15 are temporarily ignored, thus obtaining the following equivalent problem (P5).

[0033]

[0034] Where δ 1,m δ 2,m δ 3,m and δ 4,m These are all introduced auxiliary variables. I2 represents a 2x2 identity matrix. Problem (P5) is a convex optimization problem, which can be solved using convex optimization tools. The solution process for the inner optimization problem is as follows: First, initialize {a} according to the user QoS. m [0],b m [0],g m [0],j m [0]}, let k = 0, then solve (P5), after solving, let {a m [k+1],b m [k+1],g m [k+1],j m [k+1]}={a m ,b m ,g m ,j m Then let k = k + 1, and solve (P5) again until the absolute value of the difference between the two solutions (P5) is less than the threshold. Then output the result. The beamforming matrix obtained by the final solution may not have a rank of 1, so it is necessary to construct a solution with a rank of 1 through Gaussian randomization.

[0035] Furthermore, step 5) is solved based on a one-dimensional search method. The minimum transmit power for each given UAV location is solved by an inner optimization algorithm. The one-dimensional search method discretizes a certain length of one-dimensional interval, calculates the value at each discrete point, and finally finds the minimum value among all discrete points. The inner algorithm refers to solving for the minimum transmit power given the UAV location, i.e., first initializing the auxiliary variable {a} according to the user QoS. m [0],b m [0],g m [0],j m [0]}, let k = 0, then solve (P5), after solving, let {a m [k+1],b m [k+1],g m [k+1],j m [k+1]}={a m ,b m ,g m ,j m Then let k = k + 1, and solve again (P5) until the absolute value of the difference between the two solutions (P5) is less than the threshold. Then output the result. The beamforming matrix obtained by the final solution may not have a rank of 1, so it is necessary to construct a solution with a rank of 1 through Gaussian randomization.

[0036] Furthermore, step 6) is solved based on a one-dimensional search method. The minimum transmit power for each given UAV location is solved by an inner-layer optimization algorithm.

[0037] Furthermore, step 7) uses an inner-layer optimization algorithm to calculate...

[0038] Furthermore, in step 8), if the absolute value of the difference between the current transmit power and the previous transmit power is not greater than the given decision threshold, then the transmit power minimization algorithm is determined to have converged, the minimum transmit power is given, and the process ends; if the absolute value of the difference between the updated transmit power and the previous transmit power is greater than the decision threshold, then the newly calculated transmit power value is saved as the transmit power value at this time, and the process proceeds to step 4), until the transmit power meets the convergence condition, and the minimum transmit power of the UAV is given.

[0039] The advantages and beneficial effects of this invention are as follows:

[0040] This invention minimizes the transmit power of a jitter-based UAV communication system under constraints of minimum rate for each user, common rate for users, and UAV hovering area. Its innovation lies in considering the impact of UAV jitter on the channel, unlike traditional methods that do not, and combining UAV base station jitter with RSMA technology. This invention employs continuous convex approximation, semi-definite relaxation techniques, S-programming, and one-dimensional search to obtain the optimal solution. The method provided by this invention reduces transmit power compared to other resource allocation schemes that do not use the RSMA protocol, exhibiting better practicality and feasibility. The specific innovations of this invention are mainly in steps 1, 2, and 6, which combine UAV communication under jitter conditions with the RSMA protocol to create an optimization problem and solve it using convex optimization methods. Compared to using SDMA and NOMA protocols, using the RSMA protocol can reduce the UAV's transmit power, achieving energy savings. Attached Figure Description

[0041] Figure 1 This invention provides a preferred embodiment of a UAV communication system model based on RSMA under jitter conditions;

[0042] Figure 2 This is the convergence diagram of the present invention under different conditions of the number of UAV transmitting antennas and the degree of UAV jitter;

[0043] Figure 3 This invention compares the impact of the number of UAV transmitting antennas on the transmission power of two protocols under the same jitter level.

[0044] Figure 4 This is a flowchart illustrating the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0046] The technical solution of the present invention to solve the above-mentioned technical problems is:

[0047] This invention discloses a method for minimizing the transmit power of a UAV communication system based on RSMA under jitter conditions, comprising the following steps:

[0048] Step 1: Establish a model for minimizing the transmit power of a UAV communication system based on RSMA under jitter conditions.

[0049] Step 2: Rewrite the problem of minimizing the transmit power of a UAV communication system based on RSMA under jitter conditions into a bi-level programming problem.

[0050] Step 3: Initialize the drone's position And the number of iterations t = 0.

[0051] Step 4: Calculate the minimum transmit power in the t-th iteration. The minimum transmit power is solved using an inner-layer optimization algorithm.

[0052] Step 5: Solve using a one-dimensional search method The minimum transmit power for each given UAV location is solved by an inner-layer optimization algorithm.

[0053] Step 6: Solve using a one-dimensional search method The minimum transmit power for each given UAV location is solved by an inner-layer optimization algorithm.

[0054] Step 7: Calculate using inner layer optimization algorithm

[0055] Step 8: If the absolute value of the difference between the current transmit power and the previous transmit power is not greater than the given decision threshold, then the transmit power minimization algorithm is considered to have converged, the minimum transmit power is given, and the method ends; if the absolute value of the difference between the updated transmit power and the previous transmit power is greater than the decision threshold, then the newly calculated transmit power value is saved as the transmit power value at this time, and the process is repeated in step 4) until the transmit power meets the convergence condition, and the minimum transmit power of the UAV is given.

[0056] Furthermore, the RSMA-based UAV communication system transmit power minimization model (P1) under the jitter condition described in the first step is as follows:

[0057]

[0058] C5:x min ≤x U ≤x max y min ≤y U ≤y max

[0059] Where, p i Let be the beamforming vector of the private stream sent by the UAV to the i-th user. Let x represent the noise of the channel from the UAV to the m-th user. min and x max These represent the upper and lower bounds of the UAV's flight area along the x-axis, respectively, and the y-axis represents the lower bound. min and y maxLet M represent the upper and lower bounds of the drone's flight area along the y-axis, respectively. The number of users is M. h is the channel vector of the line-of-sight link from the UAV to user m, which is unaffected by UAV jitter. m,N Let m be the expected channel gain of the non-line-of-sight link from the UAV to the m-th user. with h m,N All about UAV location (x U ,y U The function p c p is the beamforming vector of the UAV common flow. m This is the beamforming vector for the private stream sent by the UAV to user m. The uncertainty in the azimuth angle of the line-of-sight link from the drone to user m caused by drone jitter, Δθ m Uncertainty in the elevation angle of the line-of-sight link from the drone to user m due to drone jitter; Ω m This represents the set of uncertainties from the UAV to the m-th user channel, including all possible departure angles; a represents a set of users; m It is the channel vector of the line-of-sight link from the UAV to user m, which is affected by the uncertainty of the azimuth angle, b m It is the channel vector of the line-of-sight link from the UAV to user m, which is affected by the uncertainty of the elevation angle, c m R is the common rate of user m. m,tot,min β is the minimum rate that user m needs to achieve. m It is the maximum value of the link jitter between the drone and user m; x min ≤x U ≤x max ,y min ≤y U ≤y max This indicates that the UAV's flight area is restricted; the optimization variable of problem (P1) is the two-dimensional position (x) of the UAV. U ,y U The objective function (P1) represents the transmit power of the UAV, with constraints C1 indicating that the achievable common rate for each user must be greater than the sum of the common rates allocated to all users, C2 representing the QoS constraint for each user, C3 representing the non-negativity constraint for the common rate for each user, C4 representing the maximum jitter of the link from the UAV to user m, and C5 representing the UAV's flight range constraint. The optimization problem (P1) is solved using continuous convex approximation, semidefinite programming, S-programming, and one-dimensional search.

[0060] Furthermore, in the second step, the problem (P1) is transformed into a bi-level programming problem (P2), where the outer variable is the two-dimensional position (x) of the UAV. U,y U The inner variable is p. c ,p m ,c m The problem (P2) is represented as follows:

[0061]

[0062] Furthermore, in the third step, the position of the drone is initialized. And the number of iterations t = 0.

[0063] Furthermore, in the fourth step, the minimum transmit power in the t-th iteration is calculated. The minimum transmit power is solved using an inner-layer optimization algorithm. The detailed description of the inner-layer optimization problem is as follows:

[0064]

[0065] In constraints C1 and C2

[0066]

[0067] Where {K m |m=1,...,M} represents the Rice factor from the UAV to different user channels, A L and A N These are the path loss factors for line-of-sight and non-line-of-sight channels, α and α, respectively. L and α N These are the path loss exponents for the line-of-sight channel and the non-line-of-sight channel, respectively. m This represents the distance between the drone and the user m. and These are the estimated azimuth and elevation angles of the departure angle between the uniform linear array at the base station and the m-th user, respectively. U This refers to the antenna spacing. λ c h is the wavelength of the carrier center frequency, and N is the number of transmitting antennas. m,N Desired channel gain for the non-line-of-sight link from UAV to the m-th user

[0068] Since the problem described above is non-convex and contains an infinite number of inequality constraints, we first introduce a set of auxiliary variables. And make Problem (P3) is equivalent to problem (P4).

[0069]

[0070] C14:rank(Q c ) = 1

[0071]

[0072] Q c Q represents the product of the beamforming vector of the common flow and its conjugate transpose. m Let represent the product of the beamforming vector of the private stream of the m-th user and its conjugate transpose. a m ,b m ,g m ,j m Both represent auxiliary variables, using This represents the set of the aforementioned auxiliary variables.

[0073] Since problem (P4) is still nonconvex, the exponential terms of constraints C2, C3, C6, and C7 are approximated by continuous convexity at the given point. First-order Taylor expansion, In the k-th iteration The value of is determined, and the infinite number of inequality constraints are transformed into linear matrix inequalities using the S-program, while temporarily ignoring the rank-one constraints of C14 and C15, thus yielding the following equivalent problem (P5).

[0074]

[0075] Where δ 1,m δ 2,m δ 3,m and δ 4,m These are all introduced auxiliary variables. I2 represents a 2x2 identity matrix. Problem (P5) is a convex optimization problem, which can be solved using convex optimization tools. The solution process for the inner optimization problem is as follows: First, initialize {a} according to the user QoS. m [0],b m [0],g m [0],j m [0]}, let k = 0, then solve (P5), after solving, let {a m [k+1],b m [k+1],g m [k+1],j m [k+1]}={a m ,b m ,g m ,j m Then let k = k + 1, and solve (P5) again until the absolute value of the difference between the two solutions (P5) is less than the threshold. Then output the result. The beamforming matrix obtained by the final solution may not have a rank of 1, so it is necessary to construct a solution with a rank of 1 through Gaussian randomization.

[0076] Furthermore, the fifth step is solved using a one-dimensional search method. The minimum transmit power for each given UAV location is solved by an inner optimization algorithm. The one-dimensional search method discretizes a certain length of one-dimensional interval, calculates the value at each discrete point, and finally finds the minimum value among all discrete points. The inner algorithm refers to solving for the minimum transmit power given the UAV location, i.e.: first, it initializes the auxiliary variable {a} according to the user QoS. m [0],b m [0],g m [0],j m [0]}, let k = 0, then solve (P5), after solving, let {a m [k+1],b m [k+1],g m [k+1],j m [k+1]}={a m ,b m ,g m ,j m Then let k = k + 1, and solve again (P5) until the absolute value of the difference between the two solutions (P5) is less than the threshold. Then output the result. The beamforming matrix obtained by the final solution may not have a rank of 1, so it is necessary to construct a solution with a rank of 1 through Gaussian randomization.

[0077] Furthermore, in the sixth step, the solution is obtained based on a one-dimensional search method. The minimum transmit power for each given UAV location is solved by an inner-layer optimization algorithm.

[0078] Furthermore, in the seventh step, an inner-layer optimization algorithm is used to calculate...

[0079] Furthermore, in the eighth step, if the absolute value of the difference between the current transmit power and the previous transmit power is not greater than the given decision threshold, then the transmit power minimization algorithm is considered to have converged, the minimum transmit power is given, and the process ends; if the absolute value of the difference between the updated transmit power and the previous transmit power is greater than the decision threshold, then the newly calculated transmit power value is saved as the transmit power value at this time, and the process proceeds to step 4), until the transmit power meets the convergence condition, and the minimum transmit power of the UAV is given.

[0080] This invention minimizes the transmit power of a jitter-based UAV communication system under constraints of minimum rate for each user, common rate for users, and UAV hovering area. Its innovation lies in considering the impact of UAV jitter on the channel, unlike traditional methods that do not, and combining UAV base station jitter with RSMA technology. This invention employs continuous convex approximation, semi-definite relaxation techniques, S-programming, and one-dimensional search to obtain the optimal solution. The method provided by this invention reduces transmit power compared to other resource allocation schemes that do not use the RSMA protocol, exhibiting better practicality and feasibility. The specific innovations of this invention are mainly in steps 1, 2, and 6, which combine UAV communication under jitter conditions with the RSMA protocol to create an optimization problem and solve it using convex optimization methods. Compared to using SDMA and NOMA protocols, using the RSMA protocol can reduce the UAV's transmit power, achieving energy savings.

[0081] This embodiment describes a method for minimizing the transmit power of a UAV communication system based on RSMA under jitter conditions. In an RSMA-based UAV network, the number of ground users M = 4, and the system noise σ 2 = -70dBm, the UAV-to-user channel follows the Ricean channel model, UAV height H = 80, and user coordinates are [-35, 15, 0], [-35, -15, 0], [35, 15, 0], [35, -15, 0]. Ricean factor K m =3dB, path loss factor A for line-of-sight channels L = -30dB, path loss factor A for non-line-of-sight channels N = -40dB, path loss exponent α for line-of-sight channels L =2.09, Path loss exponent α for non-line-of-sight channels N =3.75, base station antenna spacing b U =0.0625, carrier center frequency λ c =2.4GHz.

[0082] In this embodiment, Figure 1 The present invention provides a preferred embodiment of a UAV communication system model based on RSMA under jitter conditions. In the figure, the UAV acts as an airborne base station, sending signals to ground users for downlink communication. The UAV is equipped with N uniform linear array antennas, and the ground users are equipped with a single antenna. Figure 2 The transmit power convergence diagram obtained by the method in this embodiment is used when the number of UAV antennas and the minimum data rate required by the user are different. Figure 3 A comparison chart of the transmit power obtained under different numbers of UAV transmit antennas in NOMA and SDMA protocols with the transmit power obtained by the method in this embodiment; from Figure 2 As can be seen from this embodiment, the method converges with the increase of the number of iterations; the more antennas there are, the lower the transmission power; the higher the minimum data rate required by the user, the higher the transmission power. Figure 3 It can be seen that the transmission power of the method in this embodiment decreases as the number of UAV antennas increases. Compared with the use of NOMA and SDMA protocols, the method in this embodiment requires less transmission power.

[0083] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions.

[0084] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0085] The above embodiments should be understood as illustrative only and not as limiting the scope of protection of the present invention. After reading the description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A method for minimizing transmit power in a UAV communication system based on RSMA under jitter conditions, characterized in that, Includes the following steps: Step 1) Establish a model for minimizing the transmit power of a UAV communication system based on RSMA under jitter conditions; Step 2) Rewrite the problem of minimizing the transmit power of the RSMA-based UAV communication system under jitter conditions into a bi-level programming form; Step 3) Initialize the drone's position And the number of iterations t = 0; Step 4) Calculate the minimum transmit power in the t-th iteration. The minimum transmit power is solved using an inner-layer optimization algorithm; Step 5) Solve using a one-dimensional search method The minimum transmit power for each given UAV location is solved by the inner optimization algorithm; Step 6) Solve using a one-dimensional search method The minimum transmit power for each given UAV location is solved by the inner optimization algorithm; Step 7) Calculate using inner layer optimization algorithm Step 8) If the absolute value of the difference between the current transmit power and the previous transmit power is not greater than the given decision threshold, then the transmit power minimization algorithm is considered to have converged, the minimum transmit power is given, and the method ends; if the absolute value of the difference between the updated transmit power and the previous transmit power is greater than the decision threshold, then the newly calculated transmit power value is saved as the transmit power value at this time, and the process returns to step 4) until the transmit power meets the convergence condition, and the minimum transmit power of the UAV is given. The transmit power minimization model P1 for the RSMA-based UAV communication system under jitter conditions in step 1) is: Where, p i Let be the beamforming vector of the private stream sent by the UAV to the i-th user. Let x represent the noise of the channel from the UAV to the m-th user. min and x max These represent the upper and lower bounds of the UAV's flight area along the x-axis, respectively, and the y-axis represents the lower bound. min and y max Let M represent the upper and lower bounds of the drone's flight area along the y-axis, respectively. The number of users is M. h is the channel vector of the line-of-sight link from the UAV to user m, which is unaffected by UAV jitter. m,N Let m be the expected channel gain of the non-line-of-sight link from the UAV to the m-th user. with h m,N All about UAV location (x U ,y U The function p c p is the beamforming vector of the UAV common flow. m This is the beamforming vector for the private stream sent by the UAV to user m. The uncertainty in the azimuth angle of the line-of-sight link from the drone to user m caused by drone jitter, Δθ m Uncertainty in the elevation angle of the line-of-sight link from the drone to user m due to drone jitter; Ω m This represents the set of uncertainties from the UAV to the m-th user channel, including all possible departure angles; a represents a set of users; m It is the channel vector of the line-of-sight link from the UAV to user m, which is affected by the uncertainty of the azimuth angle, b m It is the channel vector of the line-of-sight link from the UAV to user m, which is affected by the uncertainty of the elevation angle, c m R is the common rate of user m. m,tot,min β is the minimum rate that user m needs to achieve. m It is the maximum value of the link jitter between the drone and user m; x min ≤x U ≤x max ,y min ≤y U ≤y max This indicates that the UAV is restricted to a certain flight area; the optimization variable for problem P1 is the two-dimensional position (x, y) of the UAV. U ,y U The P1 objective function represents the transmit power of the UAV, and the constraints are: the common rate allocated to each user, the beamforming vector of the UAV base station, the achievable common rate of each user being greater than the sum of the common rates allocated to all users, the QoS constraint for each user, the non-negativity constraint for the common rate of each user, the maximum jitter of the link from the UAV to user m, and the flight range constraint of the UAV. The optimization problem P1 is solved using continuous convex approximation, semidefinite programming, S-program, and one-dimensional search. In step 2), problem P1 is transformed into a bilevel programming problem P2, where the outer variable is the two-dimensional position (x) of the UAV. U ,y U The inner variable is p. c ,{p m ,c m Problem P2 is represented as follows:

2. The method for minimizing transmit power of a UAV communication system based on RSMA under jitter conditions according to claim 1, characterized in that, In step 3), the drone's position is initialized. And the number of iterations t = 0.

3. The method for minimizing transmit power of a UAV communication system based on RSMA under jitter conditions according to claim 2, characterized in that, In step 4), the minimum transmit power in the t-th iteration is calculated. The minimum transmit power is solved using an inner-layer optimization algorithm, which is described in detail below: In constraints C1 and C2 Where {K m |m=1,...,M} represents the Rice factor from the UAV to different user channels, A L and A N These are the path loss factors for line-of-sight and non-line-of-sight channels, α and α, respectively. L and α N These are the path loss exponents for the line-of-sight channel and the non-line-of-sight channel, respectively. m This represents the distance between the drone and the user m. and These are the estimated azimuth and elevation angles of the departure angle between the uniform linear array at the base station and the m-th user, respectively. U It is the antenna spacing; λ c λ is the wavelength of the carrier center frequency, and N is the number of transmitting antennas; h m,N Let UAV be the expected channel gain for the non-line-of-sight link from the m-th user; Since the problem described above is non-convex and contains an infinite number of inequality constraints, we first introduce a set of auxiliary variables. And make Problem P3 is equivalent to problem P4. Q c Q represents the product of the beamforming vector of the common flow and its conjugate transpose. m Let represent the product of the beamforming vector of the private stream of the m-th user and its conjugate transpose. a m ,b m ,g m ,j m Both represent auxiliary variables, using This represents the set of the aforementioned auxiliary variables; Since problem P4 is still nonconvex, the exponential terms of constraints C2, C3, C6, and C7 are approximated by continuous convexity at the given point. First-order Taylor expansion, In the k-th iteration The value of is determined, and the infinite number of inequality constraints are transformed into linear matrix inequalities using the S-program. At the same time, the rank-one constraints of C14 and C15 are temporarily ignored, thus obtaining the following equivalent problem P5. Where δ 1,m δ 2,m δ 3,m and δ 4,m These are all introduced auxiliary variables; I2 represents a 2x2 identity matrix. Problem P5 is a convex optimization problem, which can be solved using convex optimization tools. The solution process for the inner optimization problem is as follows: First, initialize {a} according to the user QoS. m [0],b m [0],g m [0],j m [0]}, let k = 0, then solve P5, and after solving it, let {a m [k+1],b m [k+1],g m [k+1],j m [k+1]}={a m ,b m ,g m ,j m Then let k = k + 1, and solve P5 again until the absolute value of the difference between the two solutions to P5 is less than the threshold. Then output the result. The beamforming matrix obtained by the final solution may not have a rank of 1, so it is necessary to construct a solution with a rank of 1 through Gaussian randomization.

4. The method for minimizing transmit power of a UAV communication system based on RSMA under jitter conditions according to claim 1, characterized in that, Step 5) is solved based on a one-dimensional search method. The minimum transmit power for each given UAV location is solved by an inner optimization algorithm. The one-dimensional search method discretizes a certain length of one-dimensional interval, calculates the value at each discrete point, and finally finds the minimum value among all discrete points. The inner algorithm refers to solving for the minimum transmit power given the UAV location, i.e., first initializing the auxiliary variable {a} according to the user QoS. m [0],b m [0],g m [0],j m [0]}, let k = 0, then solve P5, and after solving it, let {a m [k+1],b m [k+1],g m [k+1],j m [k+1]}={a m ,b m ,g m ,j m Then let k = k + 1, and solve P5 again until the absolute value of the difference between the two solutions to P5 is less than the threshold. Then output the result. The beamforming matrix obtained by the final solution may not have a rank of 1, so it is necessary to construct a solution with a rank of 1 through Gaussian randomization.

5. The method for minimizing transmit power of a UAV communication system based on RSMA under jitter conditions according to claim 4, characterized in that, Step 6) is solved based on a one-dimensional search method. The minimum transmit power for each given UAV location is solved by an inner-layer optimization algorithm.

6. The method for minimizing transmit power of a UAV communication system based on RSMA under jitter conditions according to claim 1, characterized in that, Step 7) uses an inner-layer optimization algorithm to calculate 7. The method for minimizing transmit power of a UAV communication system based on RSMA under jitter conditions according to claim 1, characterized in that, If, in step 8), the absolute value of the difference between the current transmit power and the previous transmit power is not greater than the given decision threshold, then the transmit power minimization algorithm is considered to have converged, the minimum transmit power is given, and the process ends; if the absolute value of the difference between the updated transmit power and the previous transmit power is greater than the decision threshold, then the newly calculated transmit power value is saved as the transmit power value at this time, and the process proceeds to step 4), until the transmit power meets the convergence condition, and the minimum transmit power of the UAV is given.

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