Unmanned aerial vehicle assisted low earth orbit satellite constellation communication method with intelligent reflector configuration

Abstract

This invention discloses a UAV-assisted low-Earth orbit (LEO) satellite constellation communication method equipped with a smart reflector, belonging to the field of wireless communication technology. The system consists of a UAV equipped with a smart reflector and multiple LEO satellites in a constellation that have established inter-satellite links, providing downlink communication services to terminals distributed within the shared coverage area of ​​the LEO satellites. The minimum information transmission rate between user devices is maximized by jointly designing the LEO satellite transmission beam, the phase shift matrix of the smart reflector, and the flight trajectory of the UAV equipped with the smart reflector. This invention, by introducing a UAV equipped with a smart reflector, provides an effective communication method for multi-satellite cooperative communication in LEO satellite constellations with high-density user devices.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, and more particularly to a method for communication of low-Earth orbit satellite constellations assisted by unmanned aerial vehicles (UAVs) with intelligent reflective surfaces. Background Technology

[0002] The sixth-generation wireless network will achieve global coverage and massive communication. In this vision, low-Earth orbit (LEO) satellite constellations play a crucial role. Due to their low orbital altitude, LEO satellites can provide shorter signal propagation delays. Simultaneously, the global coverage and inter-satellite communication capabilities of LEO satellite constellations enable the sixth-generation network to extend to remote areas that are difficult for traditional terrestrial networks to reach, achieving true global interconnectivity. However, in a satellite-to-ground propagation environment dominated by line-of-sight links, when user equipment is densely distributed in space, precoding techniques alone are insufficient to eliminate co-channel interference between users, thus failing to fully meet the on-demand and real-time capacity requirements of high-density user equipment.

[0003] To address this issue, researchers have proposed deploying drones as relays to improve system performance. However, when used as relays, the signal forwarding power consumption of drones remains a challenge to their long-duration endurance and high reliability. In recent years, the combination of smart reflectors and drones has attracted increasing attention. Compared to traditional relay methods, drones equipped with smart reflectors have lower hardware complexity and greater deployment flexibility. Furthermore, the combination of smart reflectors and drones can help create customizable wireless environments using high-frequency bands, thereby improving system performance.

[0004] Introducing drones equipped with intelligent reflectors into low-Earth orbit satellite constellation communications can significantly enhance signal coverage and quality, reduce system complexity and cost, and improve system flexibility, robustness, and spectral efficiency, thereby meeting diverse communication needs and supporting the development of emerging applications. Summary of the Invention

[0005] The purpose of this invention is to overcome the challenges of low-Earth orbit (LEO) satellite constellation communication and to propose a UAV-assisted LEO satellite constellation communication method with a smart reflector.

[0006] The specific technical solution adopted in this invention is as follows:

[0007] A method for communication of a low-Earth orbit satellite constellation assisted by an unmanned aerial vehicle (UAV) with a smart reflector, the method comprising the following steps:

[0008] S1, consisting of S satellites in a low-Earth orbit constellation equipped with N tA low-orbit satellite with a uniform two-dimensional array antenna, a drone equipped with a smart reflector, and K ground user equipment distributed within the common coverage area of ​​the S low-orbit satellites constitute a wireless communication system.

[0009] S2. Establish a UAV-assisted low-orbit satellite communication network model with intelligent reflector configuration, determine the network topology and establish a three-dimensional Cartesian coordinate system; pre-establish inter-satellite laser links between the low-orbit satellites, and obtain statistical channel status information of all satellite-to-ground communication links in the current time slot, as well as the position information of ground user equipment and UAVs through estimation or feedback;

[0010] S3. For the acquired location information of ground user equipment and UAV, as well as statistical channel status information, a joint optimization method of satellite transmission beam, intelligent reflector phase shift matrix and UAV flight trajectory is used to calculate the transmission beam of the low-orbit satellite, intelligent reflector phase shift matrix and UAV trajectory coordinates in the current time slot.

[0011] S4. Based on the obtained low-orbit satellite transmission beam, intelligent reflector phase shift matrix and UAV trajectory coordinates in the current time slot, the low-orbit satellite, intelligent reflector and UAV adjust the transmission beam, phase shift matrix and flight position accordingly. After the adjustment is completed, the low-orbit satellite uses the transmission beam to transmit signals to the ground user equipment. Part of the signal reaches the user end directly, and the other part of the signal reaches the user end after being transmitted through the intelligent reflector of the UAV.

[0012] S5. After receiving the signal, the ground user equipment decodes it to obtain the transmitted information and completes the communication service for the current time slot.

[0013] S6. After the next time slot begins, repeat S2 to S5 until the communication service for N time slots is completed, thus realizing the system's communication service throughout the entire data frame time.

[0014] As a preferred embodiment, the specific implementation method of step S2 is as follows:

[0015] S21. Using the UAV and ground user equipment as communication nodes, establish a three-dimensional Cartesian coordinate system for the communication nodes. Set the UAV charging station as the origin of the three-dimensional Cartesian coordinate system o = [0,0,0]. The x and y axes of the coordinate system are parallel to the latitude and longitude lines, respectively, and the z axis represents the altitude. The location of the k-th ground user equipment in the current time slot is denoted as q. k =[x k ,y k [0], where the location of the UAV in the current time slot is denoted as q. r =[x r ,y r ,h0], where the starting position of the drone is fixed at q0=[0,0,h0], and h0 is the fixed flight altitude of the drone;

[0016] S22. The intelligent reflective surface is placed horizontally below the drone and consists of a two-dimensional planar array of a large number of passive reflective units, containing a total of M... r A single unit capable of simultaneously reflecting and transmitting signals; the phase shift matrix of the intelligent reflector is expressed as... Where j is the imaginary unit, θ m It is the phase shift coefficient of the m-th reflecting unit, and diag{·} denotes the vector diagonalization operation;

[0017] S23. Based on the position coordinates of low-Earth orbit satellites, UAVs, and ground user equipment, and using downlink channel estimation methods, obtain the statistical channel state information from the s-th satellite to the k-th user within the current time slot, including the large-scale fading coefficient L. s,k Channel Rice factor coefficient κ s,k and line-of-sight links The statistical channel state information from the s-th satellite to the UAV, including the large-scale fading coefficient. Channel Rice factor coefficient Line-of-sight links And statistical channel state information from the UAV to the k-th ground user equipment, including the large-scale fading coefficient P. k Channel Rice factor ν k Line-of-sight links and user receiving antenna gain G k ;

[0018] S24. In the current time slot, the transmission beam of the s-th satellite to the k-th user equipment is v. s,k Introducing auxiliary variables definition This represents the channel status information of all direct links from low-Earth orbit satellites to user k. This indicates the direct link channel status information from the drone to user k. This represents the channel status information of all direct links from low-Earth orbit satellites to the UAV, v k =[v 1,k ;…;v S,k ] represents the transmit beams of all low-Earth orbit satellites to user k; where N represents t The approximate traversal rate of the k-th terrestrial user equipment in the current time slot is expressed as: (The column vector is dimensional.)

[0019] Where |·| and ||·|| represent the absolute value and the 2-norm, respectively, (·) H ⊙ denotes the conjugate transpose, and ⊙ denotes the Kronecker product. This represents the noise power received by the k-th ground user equipment.

[0020] Preferably, the specific implementation process of the joint optimization method for satellite transmission beam, intelligent reflector phase shift matrix, and UAV flight trajectory in S3 is as follows:

[0021] A joint optimization problem is constructed and solved to maximize the minimum approximate ergodic capacity among users, thereby ensuring communication fairness among users during the communication process; among them, the optimization variables include the low-Earth orbit satellite transmission beam. Intelligent reflector phase shift vector and the current time slot coordinates q of the drone r ;

[0022] Beams transmitted via low-Earth orbit satellites Intelligent reflector phase shift vector and the current time slot coordinates q of the drone r To optimize the variables, a problem is established in the wireless communication system to maximize the minimum approximate ergodic capacity among users. This problem is expressed by formula as an optimization problem P1 that satisfies constraints C1 to C6:

[0023] P1:

[0024] st:

[0025] C1:q r [0] = q0,

[0026] C2:‖q r [n]-q r [n-1]‖ 2 ≤δV max ,

[0027] C3:‖q r [n]-q0‖≤l max ,

[0028] C4:

[0029] C5:

[0030] C6:

[0031] Where, the objective function t is the minimum traversal capacity among all users, and q r [n] represents the coordinate q of the UAV at time slot index [n]. r δ represents the duration of a single time slot, V max Indicates the maximum flight speed of the drone, l max Indicates the maximum flight radius of the drone. Let represent the maximum transmit power of the s-th satellite, tr{} represent the trace of the matrix, and st represent the constraints to be satisfied;

[0032] The optimization problem shown in P1 above is transformed into three subproblems: low-orbit satellite launch beam optimization, intelligent reflector phase shift matrix optimization, and UAV flight trajectory optimization. An iterative algorithm is then used to solve these three subproblems to obtain the optimization variables V, θ, and q. r The solution.

[0033] As a preferred method, the solution to the low-Earth orbit satellite transmission beam optimization sub-problem in step S3 is as follows:

[0034] a1) Based on the fixed UAV trajectory and the phase shift matrix of the intelligent reflector, an auxiliary matrix is ​​introduced.

[0035] A k =diag 2 {a k}, B k =diag 2 {b k},

[0036] The problem of low-Earth orbit satellite beam transmission can be represented as an optimization problem P2 satisfying C7-C10 constraints:

[0037] P2:

[0038] st:

[0039] C7:

[0040] C8:

[0041] C9:

[0042] C10:

[0043] in, τ s Let be an S-dimensional column vector where the element in the s-th row is 1 and the remaining elements are 0. For N t A 3D identity matrix, where Rank(·) denotes the rank of the matrix;

[0044] A binary search algorithm is used as the solution algorithm for the optimization problem P2, and the binary search range (t) is initialized. min , t max );

[0045] b1) Let t = (t min +t max ) / 2, and initialize the iteration count l = 0;

[0046] c1) In the l-th iteration, an auxiliary variable is introduced. When l = 0, initialize in S·N t The optimization problem P2 is transformed into finding a feasible satellite transmission beam under a fixed t by using a column vector of dimensions, all elements of which are 1. After the transformation, we get the optimization problem P3 that satisfies the C11 constraint.

[0047] P3:

[0048] stC11:C8-C10.

[0049] Among them, constraint C11 is equivalent to satisfying constraints C8-C10 at the same time;

[0050] The optimization problem P3 is solved using the convex optimization tool CVX. If the solution fails, t is updated. max =t and restart from step b1); if the solution is successful and the matrix after the lth iteration is obtained. Then for Singular value decomposition yields the eigenvectors corresponding to the largest eigenvalues. Continue with step d1);

[0051] d1) Based on the l-th iteration Let l = l + 1, and repeat step c1) until the condition is met. Update t min = t, if t max -t min If the value is greater than ε, then restart from step b1); otherwise, exit the binary search algorithm to obtain the solution to the low-Earth orbit satellite launch beamforming optimization subproblem.

[0052] Preferably, the convergence thresholds ξ and ε are both set to 0.001.

[0053] As a preferred method, the solution to the sub-problem of optimizing the phase shift matrix of the intelligent reflector in step S3 is as follows:

[0054] a2) Based on the fixed UAV trajectory and low-orbit satellite transmission beam, by introducing auxiliary variable B k,l =(Φ k v l )(Φ k v l ) H v l This represents the transmit beams of all low-Earth orbit satellites to user l, and defines... This represents the phase shift vector of the intelligent reflector.

[0055] φ m,m Let φ be the m-th row and m-th column. The optimization subproblem of the phase shift matrix of the intelligent reflector is expressed as an optimization problem P4 that satisfies the constraints C12 to C15:

[0056] P4:

[0057] st:

[0058] C12:

[0059] C13:

[0060] C14:

[0061] C15:rank(φ)=1.

[0062] A binary search algorithm is used to solve the optimization problem P4, and the binary search range (t) is initialized. min , t max );

[0063] b2) Let t = (t min +t max ) / 2, and initialize the iteration count l = 0;

[0064] c2) In the l-th iteration, an auxiliary variable is introduced. When l = 0, initialize in M represents r A column vector of dimension 1s, where all elements are 1s, transforms problem P4 into solving for a feasible phase shift of a smart reflector under a fixed t. This transformation yields optimization problem P5, which satisfies the C16 constraint, as follows:

[0065] P5:

[0066] stC16:C12-C14,

[0067] Among them, constraint C16 is equivalent to simultaneously satisfying constraints C12-C14;

[0068] The optimization problem P5 is solved using a convex optimization tool. If the solution fails, t is updated. max =t and restart from step b2); if the solution is successful and the matrix φ after the lth iteration is obtained. l+1 Then for φ l+1 Singular value decomposition yields the eigenvectors corresponding to the largest eigenvalues. Continue with step d2);

[0069] d2) Based on the l-th iteration Let l = l + 1, and repeat step c2) until the condition is met. Let t min = t, if t max -t min If the value is greater than ε, restart from step b2); otherwise, exit the binary search algorithm and obtain the solution to the intelligent reflector phase shift optimization subproblem.

[0070] Preferably, the convergence thresholds ξ and ε are both set to 0.001.

[0071] As a preferred method, the solution to the UAV flight trajectory optimization sub-problem in step S3 is as follows:

[0072] a3) Based on the fixed low-orbit satellite transmission beam and the phase shift matrix of the smart reflector, auxiliary variables are introduced.

[0073] Where λ represents the wavelength of the signal transmitted by the satellite. s = 1, 2, ..., S represents the Rice factor coefficient of the channel from the s-th satellite to the UAV, where κ s,k Let Re represent the Rice factor coefficient of the channel from the s-th satellite to the k-th user, where s = 1, 2, ..., S, k = 1, 2, ..., K, and Re{} denotes the operation of taking the real part. The UAV flight trajectory optimization subproblem is expressed as an optimization problem satisfying constraints C17-C18, P6:

[0074] P6:

[0075] st:

[0076] C17:C1-C3,

[0077] C18:

[0078] Among them, constraint C17 is equivalent to simultaneously satisfying constraints C1-C3;

[0079] A binary search algorithm is used to solve the optimization problem P6, and the binary search range (t) is initialized. min , t max ).

[0080] b3) Let t = (t min +t max ) / 2, and introduce slack variables β={β1,β2,…,β K}, c = {c1, c2, ..., c K}, where β k for The upper realm, By slack variable c k exist Using a first-order Taylor expansion, the subproblem of optimizing the UAV flight trajectory is reformulated as an optimization problem P7 satisfying C17-C21:

[0081] P7:Find:q r [n],β,c

[0082] stC17:C1-C3,

[0083] C19:

[0084] C20:

[0085] C21:

[0086] The optimization problem P7 is solved using a convex optimization tool. If the solution is successful, t is updated. min =t, re-execute step b3), otherwise update t. max =t Re-execute step b3) until t max -t min ≤ε.

[0087] As a preferred option, the convergence threshold ε is set to 0.001.

[0088] Preferably, in step S3, when using an iterative algorithm to solve the three subproblems, the three subproblems need to be solved alternately in an iterative manner. When solving each subproblem, the solution variables corresponding to the other two subproblems need to be fixed, and only the solution variables of the subproblem itself need to be solved, and V, θ, and q are continuously alternated. r The optimization process continues until the algorithm converges.

[0089] Compared to existing technologies, the advantages of this invention are as follows: This invention utilizes the high maneuverability and ease of deployment of UAVs, as well as the reshaping effect of intelligent reflectors on the signal propagation environment, to assist low-Earth orbit satellite constellations in providing communication services to ground user equipment. Furthermore, it maximizes the minimum approximate ergodic rate between user equipment by jointly designing the low-Earth orbit satellite transmission beam, intelligent reflector phase shift, and UAV flight trajectory. Therefore, the optimization method proposed in this invention exhibits good convergence and superior system performance. Attached Figure Description

[0090] Figure 1 This is a block diagram of a drone-assisted low-Earth orbit satellite constellation communication system with a smart reflector.

[0091] Figure 2It is a two-dimensional trajectory map of a drone in a drone-assisted low-Earth orbit satellite constellation communication system equipped with an intelligent reflective surface;

[0092] Figure 3 This is a performance comparison of low-Earth orbit satellite constellation communication assisted by unmanned aerial vehicles (UAVs) with intelligent reflectors under different optimization schemes. Detailed Implementation

[0093] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments. Although embodiments of the present invention are shown in the drawings and below, the present invention can be implemented in many forms and is not limited to the embodiments described in the drawings and below. The accompanying drawings and the embodiments described below are provided so that the present invention can be understood more completely and accurately by those skilled in the art.

[0094] In this embodiment of the invention, the UAV-assisted low-Earth orbit satellite constellation network architecture with intelligent reflectors is as follows: Figure 1 As shown. Each low-Earth orbit satellite is equipped with N t Each user equipment is equipped with a single antenna, and the smart reflector is equipped with M... r Each low-Earth orbit (LEO) satellite has a reflector unit. Inter-satellite laser links are pre-established between LEO satellites to collaboratively acquire channel status information from the LEO satellite to the intelligent reflector and all user equipment, as well as the channel status information from the intelligent reflector to all user equipment, and the location information of the UAV and users. Based on the acquired information, the LEO satellite transmission beam and the phase shift matrix of the intelligent reflector, along with the UAV's flight trajectory, are collaboratively designed. The transmission signal is then constructed and transmitted, and the user equipment receives and decodes the signal, completing the information transmission from the LEO satellite constellation to the ground.

[0095] In an embodiment of the present invention, a method for communication of a low-Earth orbit satellite constellation assisted by a drone with a smart reflector is provided, specifically including the following steps:

[0096] S1, consisting of S satellites in a low-Earth orbit constellation equipped with N t A low-Earth orbit satellite with a uniform two-dimensional array antenna, a drone equipped with a smart reflector, and K ground user devices distributed within the coverage area of ​​the S low-Earth orbit satellites constitute a configuration such as... Figure 1 The wireless communication system shown.

[0097] It should be noted that S and N here t Both K and K need to be determined based on the actual low-orbit satellites and ground user equipment in the wireless communication system. They are not fixed parameters and are not specifically limited in this regard.

[0098] S2. Establish a UAV-assisted low-orbit satellite communication network model with intelligent reflector configuration, determine the network topology and establish a three-dimensional Cartesian coordinate system; pre-establish inter-satellite laser links between the low-orbit satellites, and obtain statistical channel status information of all satellite-to-ground communication links in the current time slot, as well as the location information of ground user equipment and UAVs, through estimation or feedback.

[0099] In this embodiment, the specific implementation method of step S2 is as follows:

[0100] S21. Using the UAV and ground user equipment as communication nodes, establish a three-dimensional Cartesian coordinate system for the communication nodes. Set the UAV charging station as the origin of the three-dimensional Cartesian coordinate system o = [0,0,0]. The x and y axes of the coordinate system are parallel to the latitude and longitude lines, respectively, and the z axis represents the altitude. The location of the k-th ground user equipment in the current time slot is denoted as q. k =[x k ,y k [0], where the location of the UAV in the current time slot is denoted as q. r =[x r ,y r ,h0], where the superscript (·) r It has no specific meaning and is only used as an identifier for the location of the drone. The starting position of the drone is fixed at q0 = [0,0,h0], where h0 is the fixed flight altitude of the drone.

[0101] S22. The intelligent reflective surface is placed horizontally below the drone. The intelligent reflective surface is a two-dimensional planar array composed of a large number of passive reflective units, containing a total of M... r A single unit capable of simultaneously reflecting and transmitting signals; the phase shift matrix of the intelligent reflector is expressed as... Where j is the imaginary unit, θ m It is the phase shift coefficient of the m-th reflecting unit, and diag{·} denotes the vector diagonalization operation;

[0102] S23. Based on the position coordinates of low-Earth orbit satellites, UAVs, and ground user equipment, and using downlink channel estimation methods, obtain the statistical channel state information from the s-th satellite to the k-th user within the current time slot, including the large-scale fading coefficient L. s,k Channel Rice factor coefficient κ s,k and line-of-sight links The statistical channel state information from the s-th satellite to the UAV, including the large-scale fading coefficient. Channel Rice factor coefficient Line-of-sight links And statistical channel state information from the UAV to the k-th ground user equipment, including the large-scale fading coefficient P. k Channel Rice factor ν k Line-of-sight links and user receiving antenna gain G k ;

[0103] S24. In the current time slot, the transmission beam of the s-th satellite to the k-th user equipment is v. s,k Introducing auxiliary variables definition This represents the channel status information of all direct links from low-Earth orbit satellites to user k. This indicates the direct link channel status information from the drone to user k. This represents the channel status information of all direct links from low-Earth orbit satellites to the UAV, v k =[v 1,k ;…;v S,k ] represents the transmit beams of all low-Earth orbit satellites to user k; where N represents t The approximate traversal rate of the k-th terrestrial user equipment in the current time slot is expressed as: (The column vector is dimensional.)

[0104] Where |·| and ||·|| represent the absolute value and the 2-norm, respectively, (·) H ⊙ denotes the conjugate transpose, and ⊙ denotes the Kronecker product. This represents the noise power received by the k-th ground user equipment.

[0105] S3. For the acquired location information of ground user equipment and UAV, as well as statistical channel status information, a joint optimization method of satellite transmission beam, intelligent reflector phase shift matrix and UAV flight trajectory is used to calculate the transmission beam of the low-orbit satellite, intelligent reflector phase shift matrix and UAV trajectory coordinates in the current time slot.

[0106] In this embodiment, the specific implementation process of the joint optimization method for satellite transmission beam, intelligent reflector phase shift matrix, and UAV flight trajectory in step S3 above is as follows:

[0107] A joint optimization problem is constructed and solved to maximize the minimum approximate ergodic capacity among users, thereby ensuring communication fairness among users during the communication process; among them, the optimization variables include the low-Earth orbit satellite transmission beam. Intelligent reflector phase shift vector and the current time slot coordinates q of the drone r .

[0108] Specifically, using low-Earth orbit satellites to transmit beams Intelligent reflector phase shift vector and the current time slot coordinates q of the drone rTo optimize the variables, a problem is established in the wireless communication system to maximize the minimum approximate ergodic capacity among users. This problem is expressed by formula as an optimization problem P1 that satisfies constraints C1 to C6:

[0109] P1:

[0110] st:

[0111] C1:q r [0] = q0,

[0112] C2:‖q r [n]-q r [n-1]‖ 2 ≤δV max ,

[0113] C3:‖q r [n]-q0‖≤l max ,

[0114] C4:

[0115] C5:

[0116] C6:

[0117] Where, the objective function t is the minimum traversal capacity among all users, and q r [n] represents the coordinate q of the UAV at time slot index [n]. r δ represents the duration of a single time slot, V max Indicates the maximum flight speed of the drone, l max Indicates the maximum flight radius of the drone. Let t represent the maximum transmit power of the s-th satellite, tr{} represent the trace of the matrix; st represent the constraints to be satisfied; constraints C1 represent the starting point constraint of the UAV, C2 represent the flight distance limit of the UAV in a single time slot, C3 represent the flight range limit of the UAV, C4 represent the satellite transmit power limit, C5 represent the phase shift limit of the intelligent reflector, and C6 represent the approximate traversal capacity limit of the user.

[0118] The optimization variables V, θ, and q in the above optimization problem P1 r Since the components are mutually coupled and non-convex, based on the idea of ​​alternating optimization, the optimization problem shown in P1 above is transformed into three subproblems: low-orbit satellite launch beam optimization, intelligent reflector phase shift matrix optimization, and UAV flight trajectory optimization. An iterative algorithm is then used to solve these three subproblems to obtain the optimization variables V, θ, and q. r The solution.

[0119] The following section details the specific steps for solving these three subproblems using an iterative algorithm. Since the three subproblems are solved iteratively, only one subproblem is solved at a time. The solution methods for each of the three subproblems are described below.

[0120] In the iterative algorithm of step S3 of this embodiment, the method for solving the sub-problem of low-Earth orbit satellite transmission beam optimization is as follows:

[0121] a1) Based on the fixed UAV trajectory and the phase shift matrix of the intelligent reflector, an auxiliary matrix is ​​introduced.

[0122] A k =diag 2 {a k}, B k =diag 2 {b k},

[0123] The problem of low-Earth orbit satellite beam transmission can be represented as an optimization problem P2 satisfying C7-C10 constraints:

[0124] P2:

[0125] st:

[0126] C7:

[0127] C8:

[0128] C9:

[0129] C10:

[0130] in, τ s Let be an S-dimensional column vector where the element in the s-th row is 1 and the remaining elements are 0. For N t A 3D identity matrix, where Rank(·) denotes the rank of the matrix;

[0131] A binary search algorithm is used as the solution algorithm for the optimization problem P2, and the binary search range (t) is initialized. min , t max );

[0132] b1) Let t = (t min +t max ) / 2, and initialize the iteration count l = 0; (It should be noted that the superscript form of l in the subsequent parameter notation represents the iteration count, which has a different meaning than the subscript form of l)

[0133] c1) In the l-th iteration, an auxiliary variable is introduced. When l = 0, initialize in S·N t The optimization problem P2 is transformed into finding a feasible satellite transmission beam under a fixed t by using a column vector of dimensions, all elements of which are 1. After the transformation, we get the optimization problem P3 that satisfies the C11 constraint.

[0134] P3:

[0135] stC11:C8-C10.

[0136] Among them, constraint C11 is equivalent to satisfying constraints C8-C10 at the same time;

[0137] In this embodiment, the convex optimization tool CVX is used to solve the optimization problem P3. If the solution fails, t is updated. max =t and restart from step b1); if the solution is successful and the matrix after the lth iteration is obtained. Then for Singular value decomposition yields the eigenvectors corresponding to the largest eigenvalues. Continue with step d1);

[0138] d1) Based on the l-th iteration Let l = l + 1, and repeat step c1) until the condition is met. Update t min = t, if t max -t min If the value is greater than ε, then restart from step b1); otherwise, exit the binary search algorithm to obtain the solution to the low-Earth orbit satellite launch beamforming optimization subproblem. In this step, the convergence thresholds ξ and ε are both set to 0.001.

[0139] In the iterative algorithm of step S3 of this embodiment, the method for solving the subproblem of optimizing the phase shift matrix of the intelligent reflector is as follows:

[0140] a2) Based on the fixed UAV trajectory and low-orbit satellite transmission beam, by introducing auxiliary variable B k,l =(Φ k v l )(Φ k v l ) H v l This represents the transmit beams of all low-Earth orbit satellites to user l, and defines... This represents the phase shift vector of the intelligent reflector.

[0141] φm,m Let φ be the m-th row and m-th column. The optimization subproblem of the phase shift matrix of the intelligent reflector is expressed as an optimization problem P4 that satisfies the constraints C12 to C15:

[0142] P4:

[0143] st:

[0144] C12:

[0145] C13:

[0146] C14:

[0147] C15:rank(φ)=1.

[0148] A binary search algorithm is used to solve the optimization problem P4, and the binary search range (t) is initialized. min , t max );

[0149] b2) Let t = (t min +t max ) / 2, and initialize the iteration count l = 0;

[0150] c2) In the l-th iteration, an auxiliary variable is introduced. When l = 0, initialize in M represents r A column vector of dimension 1s, where all elements are 1s, transforms problem P4 into solving for a feasible phase shift of a smart reflector under a fixed t. This transformation yields optimization problem P5, which satisfies the C16 constraint, as follows:

[0151] P5:

[0152] stC16:C12-C14,

[0153] Among them, constraint C16 is equivalent to simultaneously satisfying constraints C12-C14;

[0154] In this embodiment, a convex optimization tool is used to solve the optimization problem P5. If the solution fails, t is updated. max =t and restart from step b2); if the solution is successful and the matrix φ after the lth iteration is obtained. l+1 Then for φ l+1 Singular value decomposition yields the eigenvectors corresponding to the largest eigenvalues. Continue with step d2);

[0155] d2) Based on the l-th iteration Let l = l + 1, and repeat step c2) until the condition is met. Let t min = t, if t max -t min If the value is greater than ε, restart from step b2); otherwise, exit the binary search algorithm and obtain the solution to the intelligent reflector phase shift optimization subproblem. In this step, the convergence thresholds ξ and ε are both set to 0.001.

[0156] In the iterative algorithm of step S3 of this embodiment, the method for solving the UAV flight trajectory optimization sub-problem is as follows:

[0157] a3) Based on the fixed low-orbit satellite transmission beam and the phase shift matrix of the smart reflector, auxiliary variables are introduced. Where λ represents the wavelength of the signal transmitted by the satellite. s = 1, 2, ..., S represents the Rice factor coefficient of the channel from the s-th satellite to the UAV, where κ s,k Let Re represent the Rice factor coefficient of the channel from the s-th satellite to the k-th user, where s = 1, 2, ..., S, k = 1, 2, ..., K, and Re{} denotes the operation of taking the real part. The UAV flight trajectory optimization subproblem is expressed as an optimization problem satisfying constraints C17-C18, P6:

[0158] P6:

[0159] st:

[0160] C17:C1-C3,

[0161] C18:

[0162] Among them, constraint C17 is equivalent to simultaneously satisfying constraints C1-C3;

[0163] A binary search algorithm is used to solve the optimization problem P6, and the binary search range (t) is initialized. min , t max ).

[0164] b3) Let t = (t min +t max ) / 2, and introduce slack variables β={β1,β2,…,β K}, c = {c1, c2, ..., c K}, where β k for The upper realm, By slack variable c k exist Using a first-order Taylor expansion, the subproblem of optimizing the UAV flight trajectory is reformulated as an optimization problem P7 satisfying C17-C21:

[0165] P7:Find:q r [n],β,c

[0166] stC17:C1-C3,

[0167] C19:

[0168] C20:

[0169] C21:

[0170] It should be noted that the above Find:q r [n], β, c represent the search for a set of q r [n],β,c satisfy the constraints C17-C21.

[0171] In this embodiment, a convex optimization tool is used to solve the optimization problem P7. If the problem is successfully solved, t is updated. min =t, re-execute step b3), otherwise update t. max =t Re-execute step b3) until t max -t min ≤ε. The convergence threshold ε in this step is 0.001.

[0172] As previously described, the solution processes for each of the three subproblems have been introduced. Therefore, in order to find the optimal solution for these three variables in the original problem P1, this invention designs an overall optimization algorithm. This algorithm iteratively solves the three subproblems in S3 based on the idea of ​​alternating iterative optimization. It fixes two variables sequentially to optimize the third variable, and continuously performs this optimization process alternately until the algorithm converges. Since the objective function of the original problem (P1) is bounded, by iteratively solving each subproblem, the solution to the original problem can be gradually approximated, ultimately causing the overall algorithm to converge to a feasible solution.

[0173] Therefore, in this embodiment, when using an iterative algorithm to solve these three subproblems, it is necessary to iteratively solve the three subproblems alternately. When solving each subproblem, the solution variables corresponding to the other two subproblems must be fixed, and only the solution variables of the subproblem itself must be solved, continuously alternating between V, θ, and q. r The optimization process continues until the algorithm converges, and the final V, θ, q are obtained. r This can be output as a result and used to adjust the transmission beam of low-orbit satellites, the phase shift matrix of smart reflectors, and the flight position of UAVs.

[0174] S4. Based on the obtained low-orbit satellite transmission beam, intelligent reflector phase shift matrix, and UAV trajectory coordinates in the current time slot, the low-orbit satellite, intelligent reflector, and UAV adjust their transmission beams, phase shift matrices, and flight positions accordingly. After the adjustments are completed, the low-orbit satellite uses the transmission beam to transmit signals to ground user equipment. Part of the signal reaches the user end directly, and the other part of the signal reaches the user end after being transmitted through the intelligent reflector of the UAV.

[0175] S5. After receiving the signal, the ground user equipment decodes it to obtain the transmitted information and completes the communication service for the current time slot.

[0176] S6. After the next time slot begins, repeat S2 to S5 until the communication service for N time slots is completed, thus realizing the system's communication service throughout the entire data frame time.

[0177] It should be noted that the number of time slots N mentioned above is a value that is adjusted according to the actual communication service demand, and there is no limitation on it.

[0178] The computer simulation results of the above-described method of the present invention are as follows: Figure 2 As shown, the joint optimization method for low-orbit satellite transmission beam, intelligent reflector phase shift matrix, and UAV flight trajectory design proposed in this invention can effectively maximize the minimum approximate ergonomic capacity among users, thereby ensuring communication fairness among users during the communication process. The UAV equipped with the intelligent reflector takes off from the starting point and flies continuously towards the user equipment. This flight path effectively reduces signal power loss between the UAV and the user equipment. Furthermore, Figure 3 This demonstrates that the methods shown in S1 to S6 of this invention (denoted as the proposed algorithm) significantly improve the downlink transmission rate of low-Earth orbit satellites as the number of intelligent reflectors increases, while keeping the number of user devices constant. Compared to approaches that do not optimize intelligent reflectors, this invention has significant advantages.

[0179] In summary, this invention maximizes the minimum information transmission rate between user devices by jointly designing the low-Earth orbit satellite transmission beam, the phase shift matrix of the intelligent reflector, and the flight trajectory of a UAV equipped with the intelligent reflector. By introducing a UAV equipped with an intelligent reflector, this invention provides an effective communication method for multi-satellite cooperative communication in low-Earth orbit satellite constellations with high-density user devices.

[0180] The embodiments described above are merely some preferred implementations of the present invention and are not intended to limit the invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, all technical solutions obtained through equivalent substitution or transformation fall within the protection scope of the present invention.

Claims

1. A method for communication of a low-Earth orbit satellite constellation assisted by an unmanned aerial vehicle (UAV) with a smart reflector, characterized in that, The method includes the following steps: S1, consisting of S satellites in a low-Earth orbit constellation equipped with N t A low-orbit satellite with a uniform two-dimensional array antenna, a drone equipped with a smart reflector, and K ground user equipment distributed within the common coverage area of ​​the S low-orbit satellites constitute a wireless communication system. S2. Establish a UAV-assisted low-orbit satellite communication network model with intelligent reflector configuration, determine the network topology and establish a three-dimensional Cartesian coordinate system; pre-establish inter-satellite laser links between the low-orbit satellites, and obtain statistical channel status information of all satellite-to-ground communication links in the current time slot, as well as the position information of ground user equipment and UAVs through estimation or feedback; S3. For the acquired location information of ground user equipment and UAV, as well as statistical channel status information, a joint optimization method of satellite transmission beam, intelligent reflector phase shift matrix and UAV flight trajectory is used to calculate the transmission beam of the low-orbit satellite, intelligent reflector phase shift matrix and UAV trajectory coordinates in the current time slot. S4. Based on the obtained low-orbit satellite transmission beam, intelligent reflector phase shift matrix and UAV trajectory coordinates in the current time slot, the low-orbit satellite, intelligent reflector and UAV adjust the transmission beam, phase shift matrix and flight position accordingly. After the adjustment is completed, the low-orbit satellite uses the transmission beam to transmit signals to the ground user equipment. Part of the signal reaches the user end directly, and the other part of the signal reaches the user end after being transmitted through the intelligent reflector of the UAV. S5. After receiving the signal, the ground user equipment decodes it to obtain the transmitted information and completes the communication service for the current time slot. S6. After the next time slot begins, repeat S2 to S5 until the communication service for N time slots is completed, thus realizing the system's communication service throughout the entire data frame time.

2. The UAV-assisted low-Earth orbit satellite constellation communication method with intelligent reflector configuration according to claim 1, characterized in that... The specific implementation method of step S2 is as follows: S21. Using the UAV and ground user equipment as communication nodes, establish a three-dimensional Cartesian coordinate system for the communication nodes. Set the UAV charging station as the origin of the three-dimensional Cartesian coordinate system o = [0,0,0]. The x and y axes of the coordinate system are parallel to the latitude and longitude lines, respectively, and the z axis represents the altitude. The location of the k-th ground user equipment in the current time slot is denoted as q. k =[x k ,y k [0], where the location of the UAV in the current time slot is denoted as q. r =[x r ,y r ,h0], where the starting position of the drone is fixed at q0=[0,0,h0], and h0 is the fixed flight altitude of the drone; S22. The intelligent reflective surface is placed horizontally below the drone and consists of a two-dimensional planar array of a large number of passive reflective units, containing a total of M... r A single unit capable of simultaneously reflecting and transmitting signals; the phase shift matrix of the intelligent reflector is expressed as... Where j is the imaginary unit, θ m It is the phase shift coefficient of the m-th reflecting unit, and diag{·} denotes the vector diagonalization operation; S23. Based on the position coordinates of low-Earth orbit satellites, UAVs, and ground user equipment, and using downlink channel estimation methods, obtain the statistical channel state information from the s-th satellite to the k-th user within the current time slot, including the large-scale fading coefficient L. s,k Channel Rice factor coefficient κ s,k and line-of-sight links The statistical channel state information from the s-th satellite to the UAV, including the large-scale fading coefficient. Channel Rice factor coefficient Line-of-sight links And statistical channel state information from the UAV to the k-th ground user equipment, including the large-scale fading coefficient P. k Channel Rice factor ν k Line-of-sight links and user receiving antenna gain G k ; S24. In the current time slot, the transmission beam of the s-th satellite to the k-th user equipment is v. s,k Introducing auxiliary variables definition This represents the channel status information of all direct links from low-Earth orbit satellites to user k. This indicates the direct link channel status information from the drone to user k. This represents the channel status information of all direct links from low-Earth orbit satellites to the UAV, v k =[v 1,k ;…;v S,k ] represents the transmit beams of all low-Earth orbit satellites to user k; where N represents t The approximate traversal rate of the k-th terrestrial user equipment in the current time slot is expressed as: (The column vector is dimensional.) Where |·| and ||·|| represent the absolute value and the 2-norm, respectively, (·) H ⊙ denotes the conjugate transpose, and ⊙ denotes the Kronecker product. This represents the noise power received by the k-th ground user equipment.

3. The UAV-assisted low-Earth orbit satellite constellation communication method with intelligent reflective surface as described in claim 2, characterized in that, In S4, the specific implementation process of the joint optimization method for satellite transmission beam, intelligent reflector phase shift matrix, and UAV flight trajectory is as follows: A joint optimization problem is constructed and solved to maximize the minimum approximate ergodic capacity among users, thereby ensuring communication fairness among users during the communication process; among them, the optimization variables include the low-Earth orbit satellite transmission beam. Intelligent reflector phase shift vector and the current time slot coordinates q of the drone r ; Beams transmitted via low-Earth orbit satellites Intelligent reflector phase shift vector and the current time slot coordinates q of the drone r To optimize the variables, a problem is established in the wireless communication system to maximize the minimum approximate ergodic capacity among users. This problem is expressed by formula as an optimization problem P1 that satisfies constraints C1 to C6: Where, the objective function t is the minimum traversal capacity among all users, and q r [n] represents the coordinate q of the UAV at time slot index [n]. r δ represents the duration of a single time slot, V max Indicates the maximum flight speed of the drone, l max Indicates the maximum flight radius of the drone. Let represent the maximum transmit power of the s-th satellite, tr{} represent the trace of the matrix, and st represent the constraints to be satisfied; The optimization problem shown in P1 above is transformed into three subproblems: low-orbit satellite launch beam optimization, intelligent reflector phase shift matrix optimization, and UAV flight trajectory optimization. An iterative algorithm is then used to solve these three subproblems to obtain the optimization variables V, θ, and q. r The solution.

4. The UAV-assisted low-Earth orbit satellite constellation communication method with intelligent reflector configuration as described in claim 3, characterized in that... In step S3, the method for solving the sub-problem of low-Earth orbit satellite transmission beam optimization is as follows: a1) Based on the fixed UAV trajectory and the phase shift matrix of the intelligent reflector, an auxiliary matrix is ​​introduced. A k =diag 2 {a k }, B k =diag 2 {b k The problem of low-Earth orbit satellite beam transmission can be represented as an optimization problem P2 satisfying C7-C10 constraints: in, τ s Let be an S-dimensional column vector where the element in the s-th row is 1 and the remaining elements are 0. For N t A 3D identity matrix, where Rank(·) denotes the rank of the matrix; A binary search algorithm is used as the solution algorithm for the optimization problem P2, and the binary search range (t) is initialized. min , t max ); b1) Let t = (t min +t max ) / 2, and initialize the iteration count l = 0; c1) In the l-th iteration, an auxiliary variable is introduced. When l = 0, initialize in S·N t The optimization problem P2 is transformed into finding a feasible satellite transmission beam under a fixed t by using a column vector of dimensions, all elements of which are 1. After the transformation, we get the optimization problem P3 that satisfies the C11 constraint. stC11:C8-C10. Among them, constraint C11 is equivalent to satisfying constraints C8-C10 at the same time; The optimization problem P3 is solved using the convex optimization tool CVX. If the solution fails, t is updated. max =t and restart from step b1); if the solution is successful and the matrix after the lth iteration is obtained. Then for Singular value decomposition yields the eigenvectors corresponding to the largest eigenvalues. Continue with step d1); d1) Based on the l-th iteration Let l = l + 1, and repeat step c1) until the condition is met. Update t min = t, if t max -t min If the value is greater than ε, then restart from step b1); otherwise, exit the binary search algorithm to obtain the solution to the low-Earth orbit satellite launch beamforming optimization subproblem.

5. The UAV-assisted low-Earth orbit satellite constellation communication method with intelligent reflector configuration as described in claim 4, characterized in that, The convergence thresholds ξ and ε are both set to 0.

001.

6. The UAV-assisted low-Earth orbit satellite constellation communication method with intelligent reflector as described in claim 4, characterized in that, In step S3, the method for solving the sub-problem of optimizing the phase shift matrix of the intelligent reflector is as follows: a2) Based on the fixed UAV trajectory and low-orbit satellite transmission beam, by introducing auxiliary variable B k,l =(Φ k v l )(Φ k v l ) H v l This represents the transmit beams of all low-Earth orbit satellites to user l, and defines... This represents the phase shift vector of the intelligent reflector. φ m,m Let φ be the m-th row and m-th column. The optimization subproblem of the phase shift matrix of the intelligent reflector is expressed as an optimization problem P4 that satisfies the constraints C12 to C15: A binary search algorithm is used to solve the optimization problem P4, and the binary search range (t) is initialized. min , t max ); b2) Let t = (t min +t max ) / 2, and initialize the iteration count l = 0; c2) In the l-th iteration, an auxiliary variable is introduced. When l = 0, initialize in M represents r A column vector of dimension 1s, where all elements are 1s, transforms problem P4 into solving for a feasible phase shift of a smart reflector under a fixed t. This transformation yields optimization problem P5, which satisfies the C16 constraint, as follows: stC16:C12-C14, Among them, constraint C16 is equivalent to simultaneously satisfying constraints C12-C14; The optimization problem P5 is solved using a convex optimization tool. If the solution fails, t is updated. max =t and restart from step b2); if the solution is successful and the matrix φ after the lth iteration is obtained. l+1 Then for φ l+1 Singular value decomposition yields the eigenvectors corresponding to the largest eigenvalues. Continue with step d2); d2) Based on the l-th iteration Let l = l + 1, and repeat step c2) until the condition is met. Let t min = t, if t max -t min If the value is greater than ε, restart from step b2); otherwise, exit the binary search algorithm and obtain the solution to the intelligent reflector phase shift optimization subproblem.

7. The UAV-assisted low-Earth orbit satellite constellation communication method with intelligent reflector as described in claim 6, characterized in that, The convergence thresholds ξ and ε are both set to 0.

001.

8. The UAV-assisted low-Earth orbit satellite constellation communication method with intelligent reflective surface as described in claim 6, characterized in that, In step S3, the method for solving the UAV flight trajectory optimization sub-problem is as follows: a3) Based on the fixed low-orbit satellite transmission beam and the phase shift matrix of the smart reflector, auxiliary variables are introduced. Where λ represents the wavelength of the signal transmitted by the satellite. Let κ represent the Rice factor coefficient of the channel from the s-th satellite to the UAV, where κ s,k Let Re represent the Rice factor coefficient of the channel from the s-th satellite to the k-th user, where s = 1, 2, ..., S, k = 1, 2, ..., K, and Re{} denotes the operation of taking the real part. The UAV flight trajectory optimization subproblem is expressed as an optimization problem satisfying constraints C17-C18, P6: Among them, constraint C17 is equivalent to simultaneously satisfying constraints C1-C3; A binary search algorithm is used to solve the optimization problem P6, and the binary search range (t) is initialized. min , t max ); b3) Let t = (t min +t max ) / 2, and introduce slack variables β={β1,β2,...,β K }, c = {c1, c2, ..., c K }, where β k for The upper realm, By slack variable c k exist Using a first-order Taylor expansion, the subproblem of optimizing the UAV flight trajectory is reformulated as an optimization problem P7 satisfying C17-C21: The optimization problem P7 is solved using a convex optimization tool. If the solution is successful, t is updated. min =t, re-execute step b3), otherwise update t. max =t Re-execute step b3) until t max -t min ≤ε.

9. The UAV-assisted low-Earth orbit satellite constellation communication method with intelligent reflector as described in claim 8, characterized in that, The convergence threshold ε is set to 0.

001.

10. The UAV-assisted low-Earth orbit satellite constellation communication method with intelligent reflector configuration as described in claim 3, characterized in that... In step S3, when using an iterative algorithm to solve these three subproblems, it is necessary to iteratively solve the three subproblems alternately. When solving each subproblem, the solution variables corresponding to the other two subproblems must be fixed, and only the solution variables of the subproblem itself must be solved, continuously alternating between V, θ, and q. r The optimization process continues until the algorithm converges.

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