Unmanned aerial vehicle assisted low-orbit satellite constellation communication method provided with intelligent reflecting surface

Abstract

The invention discloses an unmanned aerial vehicle assisted low orbit satellite constellation communication method configured with an intelligent reflecting surface, and belongs to the technical field of wireless communication. The system is composed of an unmanned aerial vehicle carrying an intelligent reflecting surface and a plurality of low-orbit satellites in a low-orbit satellite constellation, wherein inter-satellite links are established in the low-orbit satellites. Downlink communication services are provided for terminals distributed in the common coverage range of the low-orbit satellites. The minimum information transmission rate between user equipment is maximized by jointly designing a low-orbit satellite transmitting beam, a phase shift matrix of an intelligent reflecting surface and a flight path of an unmanned aerial vehicle configured with the intelligent reflecting surface. The invention provides an effective communication method for low-orbit satellite constellation multi-satellite cooperative communication with high-density user equipment by introducing the unmanned aerial vehicle carrying the intelligent reflecting surface.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to a low-orbit satellite constellation communication method assisted by an unmanned aerial vehicle equipped with an intelligent reflective surface. Background Art

[0002] The sixth generation of wireless networks will achieve global coverage and large-scale communications. In this vision, low-orbit satellite constellations play a vital role in the sixth generation of wireless networks. Due to their low orbital altitude, low-orbit satellites can provide shorter signal propagation delays. At the same time, the global coverage and inter-satellite communication capabilities of low-orbit satellite constellations enable the sixth generation of networks to extend to remote areas that are difficult to reach with traditional terrestrial networks, achieving true global interconnection. However, in a satellite-to-ground propagation environment dominated by line-of-sight links, when user devices are densely distributed in space, it is difficult to eliminate co-channel interference between users by relying solely on precoding technology, and therefore it is impossible to fully meet the on-demand and real-time capacity requirements of user devices with high connection density.

[0003] To solve this problem, researchers have proposed improving system performance by deploying drones as relays. However, when acting as relays, the signal forwarding power consumption of drones remains a challenge to their sustainability for long-term endurance and high reliability. In recent years, the combination of smart reflective surfaces and drones has attracted increasing attention. Compared with traditional relay modes, drones equipped with smart reflective surfaces have lower hardware complexity and higher deployment flexibility. In addition, the combination of smart reflective surfaces and drones can also help create a customizable wireless environment using high frequency bands, thereby improving system performance.

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

[0005] The purpose of the present invention is to overcome the challenges of low-orbit satellite constellation communications and propose a low-orbit satellite constellation communication method assisted by a drone equipped with an intelligent reflective surface.

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

[0007] A low-orbit satellite constellation communication method assisted by a drone equipped with an intelligent reflective surface, the method comprising the following steps:

[0008] S1, a low-orbit satellite constellation with S satellites equipped with N tA low-orbit satellite with a uniform two-dimensional array antenna, a UAV equipped with an intelligent reflective surface, and K ground user devices distributed within the common coverage of the S low-orbit satellites constitute a wireless communication system.

[0009] S2. Establish a low-orbit satellite communication network model assisted by a drone equipped with an intelligent reflective surface, 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 state information of all satellite-to-ground communication links in the current time slot through estimation or feedback, as well as the location information of ground user equipment and drones;

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

[0011] S4. Based on the obtained low-orbit satellite transmission beam in the current time slot, the phase shift matrix of the intelligent reflection surface and the trajectory coordinates of the UAV, the low-orbit satellite, the intelligent reflection surface and the 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 by the intelligent reflection surface of the UAV;

[0012] S5. After receiving the signal, the ground user equipment decodes it, obtains the sent information, and completes the communication service of the current time slot;

[0013] S6. After the next time slot starts, S2 to S5 are repeatedly executed until the communication services of N time slots are completed, thereby realizing the communication services of the system within the entire data frame time.

[0014] Preferably, 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 of the communication nodes, set the UAV charging station as the origin o=[0,0,0] of the three-dimensional Cartesian coordinate system, the x and y axes of the coordinate system are parallel to the longitude and latitude lines, and the z axis represents the height; the location of the kth ground user equipment in the current time slot is recorded as q k =[x k ,y k ,0], the position of the drone in the current time slot is recorded as q r =[x r ,y r ,h 0 ], where the starting position of the drone is fixed at q 0 =[0,0,h0 ],h 0 is the fixed flight altitude of the drone;

[0016] S22, the intelligent reflective surface is placed horizontally under the drone, and is composed of a large number of passive reflective units, a two-dimensional plane array, including M r A unit that can reflect and transmit signals at the same time; the phase shift matrix of the smart reflection surface is expressed as where j is the imaginary unit, θ m is the phase shift coefficient of the mth reflection unit, diag{·} represents the vector diagonalization operation;

[0017] S23, based on the position coordinates of low-orbit satellites, drones and ground user equipment, using the downlink channel estimation method, obtain the statistical channel state information from the sth satellite to the kth user in 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 Statistical channel state information from the sth satellite to the drone, including large-scale fading coefficients Channel Rice factor coefficient Line-of-sight link And the statistical channel state information from the UAV to the kth ground user equipment, including the large-scale fading coefficient P k , channel Rice factor coefficient ν k , Line-of-sight link and the user receiving antenna gain G k ;

[0018] S24. In the current time slot, the transmission beam of the sth satellite to the kth user equipment is v s,k , introduce auxiliary variables definition represents the channel state information of the direct link from all low-orbit satellites to user k, represents the direct link channel state information from the UAV to user k, represents the channel state information of all direct links from low-orbit satellites to UAVs, v k =[v 1,k ;…;v S,k ] represents the transmission beams of all low-orbit satellites to user k; N t dimensional column vector, the approximate ergodic rate of the kth ground user equipment in the current time slot is expressed as:

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

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

[0021] Construct a joint optimization problem and solve it to maximize the minimum approximate ergodic capacity between users, so as to ensure the fairness of communication between users during the communication process; the optimization variables include the low-orbit satellite launch beam Smart reflector phase shift vector and the current time slot coordinates of the drone q r ;

[0022] Beams from low-orbit satellites Smart reflector phase shift vector and the current time slot coordinates of the drone q r In order to optimize the variables, an optimization problem of maximizing the minimum approximate ergodic capacity between users is established in the wireless communication system, which is expressed as an optimization problem P1 satisfying the constraints C1 to C6 through the formula:

[0023] P1:

[0024] st:

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

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

[0027] C3:‖q r [n]-q 0 ‖≤l max ,

[0028] C4:

[0029] C5:

[0030] C6:

[0031] Among them, the objective function t is the minimum traversal capacity among all users, q r [n] represents the coordinate q of the drone 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 maxIndicates the maximum flight radius of the drone. represents the maximum transmission power of the s-th satellite, tr{} represents the trace of the matrix; st represents the constraint condition to be satisfied;

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

[0033] Preferably, in step S3, the method for solving the low-orbit satellite transmission beam optimization sub-problem is as follows:

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

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

[0036] The low-orbit satellite beam transmission subproblem is expressed as an optimization problem P2 that satisfies the C7-C10 constraints:

[0037] P2:

[0038] st:

[0039] C7:

[0040] C8:

[0041] C9:

[0042] C10:

[0043] in, τ s is an S-dimensional column vector with the s-th row element being 1 and the remaining elements being 0, N t dimensional identity matrix, Rank(·) represents the rank of the matrix;

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

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

[0046] c1) In the first iteration, an auxiliary variable is introduced When l = 0, initialize in Indicates S·N t The optimization problem P2 is transformed into solving a feasible satellite transmission beam under a fixed t. After the transformation, the optimization problem P3 that satisfies the C11 constraint is obtained:

[0047] P3:

[0048] stC11:C8-C10.

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

[0050] Use the convex optimization tool CVX to solve the optimization problem P3. If the solution fails, update t max = t and restart from step b1); if the solution is successful and the matrix after the first round of iteration is obtained then Perform singular value decomposition to obtain the eigenvector corresponding to the maximum eigenvalue Continue to step d1);

[0051] d1) Based on the lth iteration Let l = l + 1, and repeat step c1) until the condition is satisfied. Update min = t, if t max -t min >ε, then restart from step b1), otherwise exit the binary search algorithm and obtain the solution to the LEO satellite launch beamforming optimization subproblem

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

[0053] Preferably, in step S3, the method for solving the smart reflection surface phase shift matrix optimization sub-problem is as follows:

[0054] a2) Based on the fixed UAV trajectory and low-orbit satellite launch beam, by introducing the auxiliary variable B k,l =(Φ k v l )(Φ k v l ) H , v lrepresents the transmission beams of all low-orbit satellites to user l, and defines represents the phase shift vector of the smart reflective surface,

[0055] φ m,m is the mth row and mth column of φ, and the smart reflection surface phase shift matrix optimization subproblem is expressed as the optimization problem P4 satisfying the C12~C15 constraints:

[0056] P4:

[0057] st:

[0058] C12:

[0059] C13:

[0060] C14:

[0061] C15: rank(φ)=1.

[0062] The binary search algorithm is used as the solution algorithm for the optimization problem P4, and the binary search range (t min , t max );

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

[0064] c2) In the first iteration, an auxiliary variable is introduced When l = 0, initialize in Indicates M r The dimension of the column vector is all 1, and the problem P4 is transformed into solving the feasible smart reflector phase shift under fixed t. After the transformation, the optimization problem P5 that satisfies the C16 constraint is expressed as:

[0065] P5:

[0066] stC16:C12-C14,

[0067] Among them, constraint C16 is equivalent to satisfying constraints C12-C14 at the same time;

[0068] Use convex optimization tools to solve the optimization problem P5. If the solution fails, update t max = t and restart from step b2); if the solution is successful and the matrix φ after the first round of iteration is obtained l+1 , then for φl+1 Perform singular value decomposition to obtain the eigenvector corresponding to the maximum eigenvalue Continue to step d2);

[0069] d2) Based on the lth iteration Let l = l + 1, and repeat step c2) until the condition is satisfied. Let t min = t, if t max -t min >ε, then restart from step b2); otherwise, exit the binary search algorithm and obtain the solution to the smart reflector phase shift optimization subproblem

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

[0071] Preferably, in step S3, the method for solving the sub-problem of optimizing the flight trajectory of the UAV is as follows:

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

[0073] Where λ is the wavelength of the satellite signal. s=1,2,…S represents the channel Rice factor coefficient from the sth satellite to the UAV, where κ s,k represents the channel Ricean factor coefficient from the s-th satellite to the k-th user, s=1,2,…S,k=1,2,…,K, Re{} represents the real part operation, and the UAV flight trajectory optimization subproblem is expressed as the optimization problem P6 that satisfies the constraints C17-C18:

[0074] P6:

[0075] st:

[0076] C17:C1-C3,

[0077] C18:

[0078] Among them, constraint C17 is equivalent to satisfying constraints C1-C3 at the same time;

[0079] The binary search algorithm is used as the solution algorithm for the optimization problem P6, and the binary search range (t min , t max ).

[0080] b3) Let t = (t min +t max) / 2, and introduce the slack variable β = {β 1 ,β 2 ,…,β K},c={c 1 ,c 2 …,c K}, where β k for The upper bound of By adding the slack variable c k exist Using the first-order Taylor expansion, the subproblem of drone flight trajectory optimization is reformulated as the optimization problem P7 that satisfies C17-C21:

[0081] P7:Find:q r [n],β,c

[0082] stC17:C1-C3,

[0083] C19:

[0084] C20:

[0085] C21:

[0086] Use convex optimization tools to solve the optimization problem P7. If the solution is successful, update t min = t re-execute step b3), otherwise update t max = tRe-execute step b3) until t max -t min ≤ε.

[0087] Preferably, the convergence threshold ε is 0.001.

[0088] As a preferred method, in step S3, when the iterative algorithm is used to solve the three sub-problems, it is necessary to continuously and alternately solve the three sub-problems in an iterative manner. When solving each sub-problem, it is necessary to fix the solution variables corresponding to the other two sub-problems, and only solve the solution variables of the sub-problem itself, and continuously and alternately solve V, θ, q r The optimization process continues until the algorithm converges.

[0089] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention utilizes the high mobility and easy deployment of drones and the reshaping of the signal propagation environment by intelligent reflective surfaces to assist low-orbit satellite constellations in providing communication services to ground user devices, and maximizes the lowest approximate traversal rate between user devices by jointly designing low-orbit satellite transmission beams, intelligent reflective surface phase shifts, and drone flight trajectories. Therefore, the optimization method proposed in the present invention has good convergence and better system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a block diagram of a low-orbit satellite constellation communication system assisted by a drone equipped with an intelligent reflective surface;

[0091] Figure 2 It is a two-dimensional trajectory map of drones in a low-orbit satellite constellation communication system assisted by drones equipped with intelligent reflective surfaces;

[0092] Figure 3 It is a performance comparison of low-orbit satellite constellation communications assisted by drones equipped with smart reflective surfaces under different optimization schemes. DETAILED DESCRIPTION

[0093] The present invention is further described and illustrated below in conjunction with the accompanying drawings and specific embodiments. Although the embodiments of the present invention are given in the accompanying drawings and the following, the present invention can be implemented in various forms and is not limited by the embodiments described in the accompanying drawings and the following. The accompanying drawings and the embodiments described below are provided to enable the present invention to be more completely and accurately understood by those skilled in the art.

[0094] In the embodiment of the present invention, the low-orbit satellite constellation network architecture assisted by a drone equipped with an intelligent reflective surface is as follows: Figure 1 As shown. Each low-orbit satellite is equipped with N t antennas, each user device is equipped with a single antenna, and the smart reflector is equipped with M r The intersatellite laser links are established in advance between low-orbit satellites to collaboratively obtain the channel status information from the intelligent reflector and all user devices, the channel status information from the intelligent reflector to all user devices, and the location information of the drone and the user. Then, based on the acquired information, the low-orbit satellite transmit beam and the phase shift matrix of the intelligent reflector, as well as the flight trajectory of the drone, are collaboratively designed. Then, the transmit signal is constructed and transmitted, and the user device receives and decodes the signal, completing the information transmission from the low-orbit satellite constellation to the ground.

[0095] In an embodiment of the present invention, a method for low-orbit satellite constellation communication assisted by a drone equipped with an intelligent reflective surface is provided, which specifically includes the following steps:

[0096] S1, a low-orbit satellite constellation with S satellites equipped with N t A low-orbit satellite with a uniform two-dimensional array antenna, a drone equipped with an intelligent reflector, and K ground user devices distributed within the common coverage of the S low-orbit satellites constitute the following Figure 1 The wireless communication system shown in .

[0097] It should be noted that S and N here t, 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 no specific restrictions are made on them.

[0098] S2. Establish a low-orbit satellite communication network model assisted by a drone equipped with an intelligent reflective surface, 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 state information of all satellite-to-ground communication links in the current time slot through estimation or feedback, as well as the location information of ground user equipment and drones.

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

[0100] S21. Using the UAV and ground user equipment as communication nodes, establish a three-dimensional Cartesian coordinate system of the communication nodes, set the UAV charging station as the origin o=[0,0,0] of the three-dimensional Cartesian coordinate system, the x and y axes of the coordinate system are parallel to the longitude and latitude lines, and the z axis represents the height; the location of the kth ground user equipment in the current time slot is recorded as q k =[x k ,y k ,0], the position of the drone in the current time slot is recorded as q r =[x r ,y r ,h 0 ], where superscript (·) r No specific meaning, only used as a marker for the drone's location. The drone's starting position is fixed at q 0 =[0,0,h 0 ],h 0 is the fixed flight altitude of the drone;

[0101] S22, the intelligent reflective surface is placed horizontally under the drone, and the intelligent reflective surface is a two-dimensional plane array composed of a large number of passive reflective units, including M r A unit that can reflect and transmit signals at the same time; the phase shift matrix of the smart reflection surface is expressed as where j is the imaginary unit, θ m is the phase shift coefficient of the mth reflection unit, diag{·} represents the vector diagonalization operation;

[0102] S23, based on the position coordinates of low-orbit satellites, drones and ground user equipment, using the downlink channel estimation method, obtain the statistical channel state information from the sth satellite to the kth user in 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 Statistical channel state information from the sth satellite to the drone, including large-scale fading coefficients Channel Rice factor coefficient Line-of-sight link And the statistical channel state information from the UAV to the kth ground user equipment, including the large-scale fading coefficient P k , channel Rice factor coefficient ν k , Line-of-sight link and the user receiving antenna gain G k ;

[0103] S24. In the current time slot, the transmission beam of the sth satellite to the kth user equipment is v s,k , introduce auxiliary variables definition represents the channel state information of the direct link from all low-orbit satellites to user k, represents the direct link channel state information from the UAV to user k, represents the channel state information of all direct links from low-orbit satellites to UAVs, v k =[v 1,k ;…;v S,k ] represents the transmission beams of all low-orbit satellites to user k; N t dimensional column vector, the approximate ergodic rate of the kth ground user equipment in the current time slot is expressed as:

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

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

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

[0107] Construct a joint optimization problem and solve it to maximize the minimum approximate ergodic capacity between users, so as to ensure the fairness of communication between users during the communication process; the optimization variables include the low-orbit satellite launch beam Smart reflector phase shift vector and the current time slot coordinates of the drone q r .

[0108] Specifically, a beam is launched from a low-orbit satellite. Smart reflector phase shift vector and the current time slot coordinates of the drone q r In order to optimize the variables, an optimization problem of maximizing the minimum approximate ergodic capacity between users is established in the wireless communication system, which is expressed as an optimization problem P1 satisfying the constraints C1 to C6 through the formula:

[0109] P1:

[0110] st:

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

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

[0113] C3:‖q r [n]-q 0 ‖≤l max ,

[0114] C4:

[0115] C5:

[0116] C6:

[0117] Among them, the objective function t is the minimum traversal capacity among all users, q r [n] represents the coordinate q of the drone 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. represents the maximum transmission power of the sth satellite, tr{} represents the trace of the matrix; st represents the constraints to be met; the constraint C1 represents the starting point constraint of the UAV, C2 represents the flight distance limit of a single time slot of the UAV, C3 represents the flight range limit of the UAV, C4 represents the satellite transmission power limit, C5 represents the phase shift limit of the smart reflection surface, and C6 represents the approximate traversal capacity limit of the user;

[0118] The optimization variables V, θ, q in the above optimization problem P1 are rThey are mutually coupled and non-convex. Therefore, based on the idea of ​​alternating optimization, the optimization problem shown in P1 above is transformed into three sub-problems: low-orbit satellite launch beam optimization sub-problem, smart reflection surface phase shift matrix optimization sub-problem and UAV flight trajectory optimization sub-problem. The iterative algorithm is used to solve these three sub-problems to obtain the optimization variables V, θ, q r The solution.

[0119] The following is a detailed introduction to the specific methods of using iterative algorithms to solve these three sub-problems. Since the three sub-problems are solved iteratively, only one of the sub-problems is solved each time. The following first introduces the solution methods of each of the three sub-problems.

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

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

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

[0123] The low-orbit satellite beam transmission subproblem is expressed as an optimization problem P2 that satisfies the C7-C10 constraints:

[0124] P2:

[0125] st:

[0126] C7:

[0127] C8:

[0128] C9:

[0129] C10:

[0130] in, τ s is an S-dimensional column vector with the s-th row element being 1 and the remaining elements being 0, N t dimensional identity matrix, Rank(·) represents the rank of the matrix;

[0131] The binary search algorithm is used as the solution algorithm for the optimization problem P2, and the binary search range (t min , tmax );

[0132] b1) Let t = (t min +t max ) / 2, and initialize the number of iterations l = 0; (It should be noted that the superscript l in the subsequent parameter symbols represents the number of iterations, which is different from the meaning of the subscript l)

[0133] c1) In the first iteration, an auxiliary variable is introduced When l = 0, initialize in Indicates S·N t The optimization problem P2 is transformed into solving a feasible satellite transmission beam under a fixed t. After the transformation, the optimization problem P3 that satisfies the C11 constraint is obtained:

[0134] P3:

[0135] stC11:C8-C10.

[0136] Among them, the C11 constraint is equivalent to satisfying the C8-C10 constraints 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 first round of iteration is obtained then Perform singular value decomposition to obtain the eigenvector corresponding to the maximum eigenvalue Continue to step d1);

[0138] d1) Based on the lth iteration Let l = l + 1, and repeat step c1) until the condition is satisfied. Update min = t, if t max -t min >ε, then restart from step b1), otherwise exit the binary search algorithm and obtain the solution to the LEO satellite launch beamforming optimization subproblem The convergence thresholds ξ and ε in this step are both 0.001.

[0139] In the iterative algorithm of step S3 of this embodiment, the method for solving the smart reflection surface phase shift matrix optimization subproblem is as follows:

[0140] a2) Based on the fixed UAV trajectory and low-orbit satellite launch beam, by introducing the auxiliary variable B k,l =(Φ k v l )(Φk v l ) H , v l represents the transmission beams of all low-orbit satellites to user l, and defines represents the phase shift vector of the smart reflective surface,

[0141] φ m,m is the mth row and mth column of φ, and the smart reflection surface phase shift matrix optimization subproblem is expressed as the optimization problem P4 satisfying the C12~C15 constraints:

[0142] P4:

[0143] st:

[0144] C12:

[0145] C13:

[0146] C14:

[0147] C15: rank(φ)=1.

[0148] The binary search algorithm is used as the solution algorithm for the optimization problem P4, and the binary search range (t min , t max );

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

[0150] c2) In the first iteration, an auxiliary variable is introduced When l = 0, initialize in Indicates M r The dimension of the column vector is all 1, and the problem P4 is transformed into solving the feasible smart reflector phase shift under fixed t. After the transformation, the optimization problem P5 that satisfies the C16 constraint is expressed as:

[0151] P5:

[0152] stC16:C12-C14,

[0153] Among them, constraint C16 is equivalent to satisfying constraints C12-C14 at the same time;

[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 first round of iteration is obtained l+1 , then for φ l+1 Perform singular value decomposition to obtain the eigenvector corresponding to the maximum eigenvalue Continue to step d2);

[0155] d2) Based on the lth iteration Let l = l + 1, and repeat step c2) until the condition is satisfied. Let t min = t, if t max -t min >ε, then restart from step b2); otherwise, exit the binary search algorithm and obtain the solution to the smart reflector phase shift optimization subproblem The convergence thresholds ξ and ε in this step are both 0.001.

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

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

[0158] P6:

[0159] st:

[0160] C17:C1-C3,

[0161] C18:

[0162] Among them, constraint C17 is equivalent to satisfying constraints C1-C3 at the same time;

[0163] The binary search algorithm is used as the solution algorithm for the optimization problem P6, and the binary search range (t min , t max ).

[0164] b3) Let t = (t min +t max) / 2, and introduce the slack variable β = {β 1 ,β 2 ,…,β K},c={c 1 ,c 2 …,c K}, where β k for The upper bound of By adding the slack variable c k exist Using the first-order Taylor expansion, the subproblem of drone flight trajectory optimization is reformulated as the optimization problem P7 that satisfies 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 means to find a set of q r [n],β,c so that it satisfies the constraints C17-C21.

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

[0172] As mentioned above, the solution process of each of the three sub-problems has been introduced. Therefore, in order to find the optimal solution of the three variables in the original problem P1, the present invention designs an overall optimization algorithm. The algorithm iteratively solves the three sub-problems in S3 based on the idea of ​​alternating iterative optimization, fixes two variables in turn to optimize the third variable, and continuously alternates this optimization process until the algorithm converges. Since the objective function of the original problem (P1) is bounded, by iteratively solving each sub-problem, the solution of the original problem can be gradually approached, and finally the overall algorithm converges to a feasible solution.

[0173] Therefore, in this embodiment, when using an iterative algorithm to solve the three sub-problems, it is necessary to continuously and alternately solve the three sub-problems in an iterative manner. When solving each sub-problem, it is necessary to fix the solution variables corresponding to the other two sub-problems, and only solve the solution variables of the sub-problem itself, and continuously and alternately solve V, θ, q r The optimization process is continued until the algorithm converges, and the final V, θ, q r The result can be outputted to adjust the transmission beam of low-orbit satellites, the phase shift matrix of smart reflective surfaces, and the flight position of UAVs.

[0174] S4. Based on the obtained low-orbit satellite transmission beam in the current time slot, the phase shift matrix of the intelligent reflection surface and the trajectory coordinates of the UAV, the low-orbit satellite, the intelligent reflection surface and the 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 by the intelligent reflection surface of the UAV.

[0175] S5. After receiving the signal, the ground user equipment decodes it, obtains the sent information, and completes the communication service of the current time slot.

[0176] S6. After the next time slot starts, S2 to S5 are repeatedly executed until the communication services of N time slots are completed, thereby realizing the communication services of the system within the entire data frame time.

[0177] It should be noted that the above-mentioned number of time slots N is a value adjusted according to actual communication service requirements and is not limited thereto.

[0178] The computer simulation results of the above method of the embodiment of the present invention are as follows: Figure 2 As shown, it can be seen that the joint optimization method of low-orbit satellite transmission beam, smart reflective surface phase shift matrix and UAV flight trajectory design proposed in the present invention can effectively maximize the minimum approximate ergodic capacity between users, thereby ensuring the fairness of communication between users during the communication process. The UAV equipped with a smart reflective surface takes off from the starting point and flies continuously towards the user device. This flight route can effectively reduce the signal power loss between the UAV and the user device. In addition, Figure 3 It shows that the method shown in S1 to S6 (referred to as the proposed algorithm) proposed in the present invention can significantly improve the downlink transmission rate of low-orbit satellites as the number of smart reflective surfaces increases when the number of user devices remains unchanged. Compared with the method of not optimizing the smart transmitting surface, the present invention has obvious advantages.

[0179] In summary, the present invention maximizes the minimum information transmission rate between user devices by jointly designing the low-orbit satellite transmission beam, the phase shift matrix of the smart reflector, and the flight trajectory of the drone equipped with the smart reflector. The present invention provides an effective communication method for multi-satellite cooperative communication of low-orbit satellite constellations with high-density user devices by introducing drones equipped with smart reflectors.

[0180] The above-described embodiments are only some preferred implementations of the present invention, but are not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A low-orbit satellite constellation communication method assisted by a drone equipped with an intelligent reflective surface, characterized in that: The method comprises the following steps: S1, a low-orbit satellite constellation with S satellites equipped with N t A low-orbit satellite with a uniform two-dimensional array antenna, a UAV equipped with an intelligent reflective surface, and K ground user devices distributed within the common coverage of the S low-orbit satellites constitute a wireless communication system. S2. Establish a low-orbit satellite communication network model assisted by a drone equipped with an intelligent reflective surface, 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 state information of all satellite-to-ground communication links in the current time slot through estimation or feedback, as well as the location information of ground user equipment and drones; S3. For the acquired location information of the ground user equipment and the UAV, as well as the statistical channel state information, a satellite transmission beam, a smart reflection surface phase shift matrix and a UAV flight trajectory joint optimization method are used to calculate the transmission beam of the low-orbit satellite, the smart reflection surface phase shift matrix and the trajectory coordinates of the UAV in the current time slot; S4. Based on the obtained low-orbit satellite transmission beam in the current time slot, the phase shift matrix of the intelligent reflection surface and the trajectory coordinates of the UAV, the low-orbit satellite, the intelligent reflection surface and the 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 by the intelligent reflection surface of the UAV; S5. After receiving the signal, the ground user equipment decodes it, obtains the sent information, and completes the communication service of the current time slot; S6. After the next time slot starts, S2 to S5 are repeatedly executed until the communication services of N time slots are completed, thereby realizing the communication services of the system within the entire data frame time.

2. The method for low-orbit satellite constellation communication assisted by a drone equipped with an intelligent reflective surface 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 of the communication nodes, set the UAV charging station as the origin o=[0,0,0] of the three-dimensional Cartesian coordinate system, the x and y axes of the coordinate system are parallel to the longitude and latitude lines, and the z axis represents the height; the location of the kth ground user equipment in the current time slot is recorded as q k =[x k ,y k ,0], the position of the drone in the current time slot is recorded as q r =[x r ,y r ,h0], where the starting position of the UAV is fixed at q0 = [0,0,h0], and h0 is the fixed flight altitude of the UAV; S22, the intelligent reflective surface is placed horizontally under the drone, and is composed of a large number of passive reflective units, a two-dimensional plane array, including M r A unit that can reflect and transmit signals at the same time; the phase shift matrix of the smart reflection surface is expressed as where j is the imaginary unit, θ m is the phase shift coefficient of the mth reflection unit, diag{·} represents the vector diagonalization operation; S23, based on the position coordinates of low-orbit satellites, drones and ground user equipment, using the downlink channel estimation method, obtain the statistical channel state information from the sth satellite to the kth user in 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 Statistical channel state information from the sth satellite to the drone, including large-scale fading coefficients Channel Rice factor coefficient Line-of-sight link And the statistical channel state information from the UAV to the kth ground user equipment, including the large-scale fading coefficient P k , channel Rice factor coefficient ν k , Line-of-sight link and the user receiving antenna gain G k ; S24. In the current time slot, the transmission beam of the sth satellite to the kth user equipment is v s,k , introduce auxiliary variables definition represents the channel state information of the direct link from all low-orbit satellites to user k, represents the direct link channel state information from the UAV to user k, represents the channel state information of all direct links from low-orbit satellites to UAVs, v k =[v 1,k ;…;v S,k ] represents the transmission beams of all low-orbit satellites to user k; N t dimensional column vector, the approximate ergodic rate of the kth ground user equipment in the current time slot is expressed as: where |·| and ||·|| represent the absolute value and 2-norm respectively, (·) H represents the conjugate transpose, ⊙ represents the Kronecker product, represents the noise power received by the kth ground user equipment.

3. The method for low-orbit satellite constellation communication assisted by a drone equipped with an intelligent reflective surface according to claim 2, characterized in that: In S4, the specific implementation process of the joint optimization method of satellite transmission beam, smart reflection surface phase shift matrix and UAV flight trajectory is as follows: Construct a joint optimization problem and solve it to maximize the minimum approximate ergodic capacity between users, so as to ensure the fairness of communication between users during the communication process; the optimization variables include the low-orbit satellite launch beam Smart reflector phase shift vector and the current time slot coordinates of the drone q r ; Beams from low-orbit satellites Smart reflector phase shift vector and the current time slot coordinates of the drone q r In order to optimize the variables, an optimization problem of maximizing the minimum approximate ergodic capacity between users is established in the wireless communication system, which is expressed as an optimization problem P1 satisfying the constraints C1 to C6 through the formula: Among them, the objective function t is the minimum traversal capacity among all users, q r [n] represents the coordinate q of the drone 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. represents the maximum transmission power of the s-th satellite, tr{} represents the trace of the matrix; st represents the constraint condition to be satisfied; The optimization problem shown in P1 above is transformed into three sub-problems: low-orbit satellite launch beam optimization sub-problem, smart reflector phase shift matrix optimization sub-problem and UAV flight trajectory optimization sub-problem. The iterative algorithm is used to solve these three sub-problems to obtain the optimization variables V, θ, q r The solution.

4. The method for low-orbit satellite constellation communication assisted by a drone equipped with an intelligent reflective surface as claimed in claim 3, characterized in that In step S3, the method for solving the low-orbit satellite transmission beam optimization sub-problem is as follows: a1) Based on the fixed UAV trajectory and the intelligent reflection surface phase shift matrix, an auxiliary matrix is ​​introduced A k =diag 2 {a k }, B k =diag 2 {b k }, the low-orbit satellite beam transmission subproblem is expressed as the optimization problem P2 that satisfies the C7-C10 constraints: in, τ s is an S-dimensional column vector with the s-th row element being 1 and the remaining elements being 0, N t dimensional identity matrix, Rank(·) represents the rank of the matrix; The binary search algorithm is used as the solution algorithm for the optimization problem P2, and the binary search range (t min , t max ); b1) Let t = (t min +t max ) / 2, and initialize the number of iterations l = 0; c1) In the first iteration, an auxiliary variable is introduced When l = 0, initialize in Indicates S·N t The optimization problem P2 is transformed into solving a feasible satellite transmission beam under a fixed t. After the transformation, the optimization problem P3 that satisfies the C11 constraint is obtained: stC11:C8-C10. Among them, the C11 constraint is equivalent to satisfying the C8-C10 constraints at the same time; Use the convex optimization tool CVX to solve the optimization problem P3. If the solution fails, update t max = t and restart from step b1); if the solution is successful and the matrix after the first round of iteration is obtained then Perform singular value decomposition to obtain the eigenvector corresponding to the maximum eigenvalue Continue to step d1); d1) Based on the lth iteration Let l = l + 1, and repeat step c1) until the condition is satisfied. Update min = t, if t max -t min >ε, then restart from step b1), otherwise exit the binary search algorithm and obtain the solution to the LEO satellite launch beamforming optimization subproblem 5. The method for low-orbit satellite constellation communication assisted by a drone equipped with an intelligent reflective surface as claimed in claim 4, characterized in that: The convergence thresholds ξ and ε are both set to 0.

001.

6. The method for low-orbit satellite constellation communication assisted by a drone equipped with an intelligent reflective surface as claimed in claim 4, characterized in that: In step S3, the method for solving the smart reflection surface phase shift matrix optimization sub-problem is as follows: a2) Based on the fixed UAV trajectory and low-orbit satellite launch beam, by introducing the auxiliary variable B k,l =(Φ k v l )(Φ k v l ) H , v l represents the transmission beams of all low-orbit satellites to user l, and defines represents the phase shift vector of the smart reflective surface, φ m,m is the mth row and mth column of φ, and the smart reflection surface phase shift matrix optimization subproblem is expressed as the optimization problem P4 satisfying the C12~C15 constraints: The binary search algorithm is used as the solution algorithm for the optimization problem P4, and the binary search range (t min , t max ); b2) Let t = (t min +t max ) / 2, and initialize the number of iterations l = 0; c2) In the first iteration, an auxiliary variable is introduced When l = 0, initialize in Indicates M r The dimension of the column vector is all 1, and the problem P4 is transformed into solving the feasible smart reflection surface phase shift under fixed t. After the transformation, the optimization problem P5 that satisfies the C16 constraint is expressed as: stC16:C12-C14, Among them, constraint C16 is equivalent to satisfying constraints C12-C14 at the same time; Use convex optimization tools to solve the optimization problem P5. If the solution fails, update t max = t and restart from step b2); if the solution is successful and the matrix φ after the first round of iteration is obtained l+1 , then for φ l+1 Perform singular value decomposition to obtain the eigenvector corresponding to the maximum eigenvalue Continue to step d2); d2) Based on the lth iteration Let l = l + 1, and repeat step c2) until the condition is satisfied. Let t min = t, if t max -t min >ε, then restart from step b2); otherwise, exit the binary search algorithm and obtain the solution to the smart reflector phase shift optimization subproblem 7. The method for low-orbit satellite constellation communication assisted by a drone equipped with an intelligent reflective surface as claimed in claim 6, characterized in that: The convergence thresholds ξ and ε are both set to 0.

001.

8. The method for low-orbit satellite constellation communication assisted by a drone equipped with an intelligent reflective surface as claimed in claim 6, characterized in that: In step S3, the method for solving the sub-problem of optimizing the flight trajectory of the UAV is as follows: a3) Based on the fixed low-orbit satellite transmission beam and the smart reflector phase shift matrix, by introducing auxiliary variables Where λ is the wavelength of the satellite signal. represents the channel Ricean factor coefficient from the sth satellite to the UAV, where κ s,k represents the channel Ricean factor coefficient from the s-th satellite to the k-th user, s=1,2,...S,k=1,2,...,K, Re{} represents the real part operation, and the UAV flight trajectory optimization subproblem is expressed as the optimization problem P6 that satisfies the constraints C17-C18: Among them, constraint C17 is equivalent to satisfying constraints C1-C3 at the same time; The binary search algorithm is used as the solution algorithm for the optimization problem P6, and the binary search range (t 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 bound of By adding the slack variable c k exist Using the first-order Taylor expansion, the subproblem of drone flight trajectory optimization is reformulated as the optimization problem P7 that satisfies C17-C21: Use convex optimization tools to solve the optimization problem P7. If the solution is successful, update t min = t re-execute step b3), otherwise update t max = tRe-execute step b3) until t max -t min ≤ε.

9. The method for low-orbit satellite constellation communication assisted by a drone equipped with a smart reflective surface as claimed in claim 8, characterized in that: The convergence threshold ε is set to 0.

001.

10. The method for low-orbit satellite constellation communication assisted by a drone equipped with an intelligent reflective surface as claimed in claim 3, characterized in that In step S3, when using an iterative algorithm to solve the three sub-problems, it is necessary to continuously and alternately solve the three sub-problems in an iterative manner. When solving each sub-problem, it is necessary to fix the solution variables corresponding to the other two sub-problems, and only solve the solution variables of the sub-problem itself, and continuously and alternately solve V, θ, q r The optimization process continues until the algorithm converges.

Images