Hybrid multiple access method suitable for low earth orbit satellite internet of things

Abstract

The invention discloses a hybrid multiple access method suitable for a low-orbit satellite Internet of Things, and the method comprises the steps: firstly, enabling a low-orbit satellite and an unmanned plane to obtain the position information of ground Internet of Things equipment; secondly, on the basis of the obtained position information, calculating a position correlation coefficient between the devices, and performing grouping optimization on the multiple pieces of non-direct-transmission Internet of Things devices; then, the unmanned aerial vehicle relay serves one non-direct transmission device group in each time slot by adopting a method of combining polling scheduling with a beam forming technology based on position information; and finally, a plurality of direct transmission Internet of Things devices share spectrum resources with the unmanned aerial vehicle, a multi-beam non-orthogonal multiple access technology based on weighted beam superposition is adopted to access a satellite network at the same time, and the hybrid multiple access method provided by the invention can make full use of resources such as a time domain, a space domain and a power domain. The heterogeneous flexible access requirements of different types of Internet of Things equipment are effectively met, and the method has the advantage of low algorithm complexity, so that the system overhead is remarkably reduced, and the method is suitable for a low-orbit satellite Internet of Things access scene.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite communications, and in particular to a hybrid multiple access method suitable for a low-orbit satellite Internet of Things. Background Art

[0002] As an effective supplement to the ground mobile communication network, low-orbit satellites have attracted extensive attention and research in the field of the Internet of Things due to their lower orbital altitude and smaller transmission delay. However, as the number of IoT devices is gradually increasing and they are widely distributed around the world, how to efficiently achieve flexible access has become one of the main challenges facing low-orbit satellite IoT communications.

[0003] In traditional satellite communication access technology, orthogonal methods such as time division multiple access or frequency division multiple access are usually used. These methods may bring about problems such as low spectrum utilization, severe interference, and low device access efficiency when resources are limited. Especially for the case of heterogeneous access of IoT devices, such as devices that need to maintain low power consumption for a long time and devices with high-speed transmission, the traditional single access method may not be able to flexibly allocate resources to meet their access needs, resulting in a decrease in the quality of communication services. On the other hand, with the development of drone technology, drones are gradually being used as relay devices in low-orbit satellite IoT. Drones can obtain the location information of ground devices in real time and improve the communication coverage through relay forwarding and other means. However, the existing drone-assisted satellite communication system does not fully utilize the real-time location information of the device and the location correlation between devices to effectively schedule and optimize resources.

[0004] Therefore, how to make full use of time domain, space domain and power domain resources in the low-orbit satellite Internet of Things system to provide heterogeneous access requirements for different types of devices and further improve spectrum efficiency has become a technical problem to be solved. In view of the above problems, the present invention proposes a hybrid multiple access method suitable for low-orbit satellite Internet of Things. Summary of the invention

[0005] Purpose of the invention: To provide a hybrid multiple access method suitable for low-orbit satellite Internet of Things, the method is for a low-orbit satellite Internet of Things communication network, wherein multiple non-direct transmission Internet of Things devices that cannot communicate directly with the satellite are forwarded through a drone relay, and are grouped and optimized according to the calculated position correlation coefficient between the devices; on the basis of grouping, the drone relay uses a method of polling scheduling combined with a beamforming technology based on location information to serve a non-direct transmission device group in each time slot; for multiple direct transmission devices that can communicate directly with the satellite, a multi-beam non-orthogonal multiple access technology based on weighted beam superposition is used, and spectrum resources are shared with drones to jointly access the satellite network, thereby effectively meeting the heterogeneous access requirements of different types of Internet of Things devices.

[0006] Technical solution:

[0007] A hybrid multiple access method applicable to a low-orbit satellite Internet of Things comprises a ground Internet of Things device and an access network, wherein the access network comprises an unmanned aerial vehicle network and a low-orbit satellite network, wherein the ground Internet of Things device is configured with an omnidirectional antenna, wherein the ground Internet of Things device comprises a direct transmission device and a non-direct transmission device, wherein the unmanned aerial vehicle network comprises an unmanned aerial vehicle relay network and an unmanned aerial vehicle, wherein the non-direct transmission device forwards a signal to the low-orbit satellite network through the unmanned aerial vehicle relay network, wherein the direct transmission device shares spectrum resources with the unmanned aerial vehicle and simultaneously accesses the low-orbit satellite network, wherein the method comprises the following steps:

[0008] Step 1: The low-orbit satellite network and the drone relay network obtain the location information of the ground IoT device through a location estimation method;

[0009] Step 2: Based on the acquired location information, calculate the location correlation coefficient between the non-direct transmission devices, and optimize the non-direct transmission devices by grouping according to the size of the correlation coefficient to obtain a plurality of non-direct transmission groups;

[0010] Step 3: The UAV relay network uses a beamforming technology based on location information combined with a polling scheduling algorithm to serve a non-direct transmission group in each time slot;

[0011] Step 4: The direct transmission device shares spectrum resources with the UAV and simultaneously accesses the low-orbit satellite network using a multi-beam non-orthogonal multiple access technology based on weighted beam superposition.

[0012] In a further embodiment, in step 1, the position estimation method includes Beidou satellite navigation system, global navigation satellite system and global positioning system;

[0013] The position estimation method is expressed as follows:

[0014] The location information of the i-th ground IoT device is expressed as (X i ,Y i ,0), the location information of the access network is expressed as (X l ,Y l ,H l ),l∈{s,u};

[0015] Wherein, s is the low-orbit satellite network, and u is the UAV relay network;

[0016] Calculate the angle information of the i-th ground IoT device relative to the access network, where the angle information includes the angle of arrival, which is represented by θ i ;

[0017] Based on the calculated angle information, the channel vector between the i-th terrestrial IoT device and the access network is expressed as follows:

[0018] h i =L i β i a(θ i );

[0019] Among them, L i represents the path loss, which is calculated in dB as follows:

[0020]

[0021] Among them, d i represents the distance from the ith IoT device to the access network, and d i It is expressed as follows;

[0022]

[0023] Among them, β i Indicates small-scale fading. The UAV relay network channel obeys the Nakagami-m distribution, while the low-orbit satellite network channel obeys the shadow Rice distribution.

[0024] In a further embodiment, the drone relay network is configured with a uniform linear array antenna, and the array steering vector of the uniform linear array is represented as follows:

[0025]

[0026] Where λ is the wavelength, d a is the antenna element spacing, N U The number of uniform linear array antennas configured for the drone.

[0027] In a further embodiment, in step 2, based on the channel vector calculated from the position information, the position correlation coefficients of the i-th and j-th non-direct transmission devices are calculated as follows:

[0028]

[0029] in(·) T Indicates transpose.

[0030] In a further embodiment, the GK non-direct transmission devices are divided into G groups, each group having K devices, and the step 2 specifically includes the following steps:

[0031] Step 2-1: Calculate the correlation coefficient between the indirect transmission device and the indirect transmission group, randomly select a device from the GK indirect transmission devices and assign it to the first group, and calculate the correlation coefficient between any remaining unassigned device j and the known device group G i The position correlation coefficient of

[0032] Step 2-2: If the calculation result of the correlation system is less than the preset threshold value ρ th , that is, ij <ρ th , then assign the device j to the device group G i Otherwise, assign device j to a new empty set device group;

[0033] Step 2-3: Repeat steps 2-1 and 2-2 until all non-direct transmission devices are grouped.

[0034] In a further embodiment, step 3 further includes establishing an optimization problem, which is expressed as follows:

[0035]

[0036] st||w k ||=1,k=1,2,…,K;

[0037] in, p k and p j represents the transmission power of the IoT device; w k represents the UAV receive beamforming weight vector; represents the variance of additive white Gaussian noise.

[0038] In a further embodiment, based on the known location information of the non-direct transmission device in the drone relay network, and the drone receiving beamforming weight vector w k Independent of each other, the optimization problem can be transformed into:

[0039]

[0040] st||w k ||=1,k=1,2,…,K;

[0041] According to the generalized Rayleigh quotient, the optimized beamforming weight vector is calculated as follows:

[0042]

[0043] In a further embodiment, in step 4, after the drone relay network successfully decodes the received signal of the non-direct transmission device, it forwards the re-encoded signal to the low-orbit satellite network, and the drone shares spectrum resources with multiple directly connected devices and accesses the low-orbit satellite network at the same time. In the tth time slot, the signal received by the low-orbit satellite network is expressed as:

[0044]

[0045] Among them, p s ,s∈{0,1,2,…,M} represents the transmission power of the UAV and the direct transmission device; v represents the receiving simulation beamforming weight vector of the low-orbit satellite network; h s represents the channel vector, n(t) represents additive white Gaussian noise;

[0046] The low-orbit satellite network is configured with a phased array antenna, and uses a multi-beam non-orthogonal multiple access technology based on weighted beam superposition to simultaneously receive signals sent from the M direct transmission devices and the drone;

[0047] The receive beam is the weighted sum of M+1 simulated beams, expressed as follows:

[0048] Uv=ω0v0+ω1v1…+ω m v m +…+ω M v M ;

[0049] Wherein, v represents the receiving analog beamforming weight vector of the low-orbit satellite network; U represents the coefficient matrix satisfying the constant modulus constraint of the phased array; ω s ,s∈{0,1,2,L,M} represents the weighting factor, satisfying ω0+ω1+L+ω m +L+ω M =1; and Respectively represent the receiving simulation beamforming weight vectors of the signals sent by the UAV and the mth direct transmission device, N S Indicates the number of phased array antennas configured in the low-orbit satellite network.

[0050] In a further embodiment, the coefficient matrix U comprises a diagonal matrix, and the diagonal elements of the diagonal matrix are calculated as follows:

[0051]

[0052] in,

[0053] x=ω0cosb0+ω1cosb1+…+ω m cosbm +…+ω M cosb M ;

[0054] y=ω0sinb0+ω1sinb1+…+ω m sinb m +…+ω M sinb M ;

[0055] b s =(n-1)πcosθ s ,s∈{0,1,2,…,M};

[0056] The receive simulation beamforming weight vector v of the low-orbit satellite network is calculated as follows:

[0057]

[0058] in, acrcos(·) represents the arc cosine function.

[0059] In a further embodiment, by adjusting the weighting factor ω s , s∈{0,1,2,L,M}, sort the channel quality of the UAV and the direct transmission device that need to access the low-orbit satellite network to meet x0→x1→L→x M The decoding order of

[0060] Therefore, the output signal-to-interference-noise ratios of the UAV and the mth direct transmission device obtained in the low-orbit satellite network are:

[0061]

[0062] The obtained output signal to interference plus noise ratio is compared with a preset decoding threshold, and if the output signal to interference plus noise ratio is greater than the preset decoding threshold, it indicates that the signal is successfully transmitted.

[0063] Beneficial effects of the present invention: A hybrid multiple access method for low-orbit satellite Internet of Things provided by the present invention combines the advantages of low-orbit satellites and drones, uses location information to optimize grouping of non-direct transmission devices, and optimizes the receiving beamforming of satellites and drones respectively, thereby obtaining an efficient and flexible access method for heterogeneous Internet of Things devices. It can make full use of resources such as time domain, spatial domain and power domain, thereby significantly improving the spectrum utilization efficiency of the satellite Internet of Things, and ensuring fairness and rationality among different devices, meeting the heterogeneous service requirements of different types of Internet of Things devices. Furthermore, the device grouping and beamforming algorithm provided by the present invention effectively reduce the computational complexity and reduce the signal processing overhead, thereby reducing the computational burden and overhead of the system, and improving the applicability and scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the satellite Internet of Things heterogeneous device access communication system of the present invention.

[0065] Figure 2 It is a flow chart of a specific implementation method of the present invention. DETAILED DESCRIPTION

[0066] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0067] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0068] Reference Figure 1-2 , a hybrid multiple access method applicable to low-orbit satellite Internet of Things disclosed in the present invention, including a ground Internet of Things device and an access network, the access network including a drone network and a low-orbit satellite network, the ground Internet of Things device is configured with an omnidirectional antenna, the ground Internet of Things device includes a direct transmission device and a non-direct transmission device, the direct transmission device includes a ground Internet of Things device directly connected to the low-orbit satellite network, the non-direct transmission device includes a ground Internet of Things device indirectly connected to the low-orbit satellite network, the drone network includes a drone relay network and a drone, the non-direct transmission device forwards a signal to the low-orbit satellite network through the drone relay network, the direct transmission device shares spectrum resources with the drone, and simultaneously accesses the low-orbit satellite network to achieve heterogeneous and flexible access of different types of devices, the method includes the following steps:

[0069] Step 1: The low-orbit satellite network and the drone relay network obtain the location information of the ground IoT device through a location estimation method;

[0070] In the step 1, the position estimation method includes the Beidou satellite navigation system, the global navigation satellite system and the global positioning system;

[0071] The position estimation method is expressed as follows:

[0072] The location information of the i-th ground IoT device is expressed as (X i ,Y i ,0), the location information of the access network is expressed as (X l ,Y l ,H l ),l∈{s,u};

[0073] Wherein, s is the low-orbit satellite network, and u is the UAV relay network;

[0074] Calculate the angle information of the i-th ground IoT device relative to the access network, i.e., the low-orbit satellite network or the drone relay network, where the angle information includes the angle of arrival, which is represented by θ i ;

[0075] Based on the angle information obtained by calculation, the channel vector between the i-th ground IoT device and the access network, i.e., the low-orbit satellite network or the drone relay network, is expressed as follows:

[0076] h i =L i β i a(θ i );

[0077] Among them, L i represents the path loss, which is calculated in dB as follows:

[0078]

[0079] Among them, d i represents the distance from the ith IoT device to the access network, and d i It is expressed as follows;

[0080]

[0081] Among them, β i Indicates small-scale fading. The UAV relay network channel obeys the Nakagami-m distribution, while the low-orbit satellite network channel obeys the shadow Rice distribution.

[0082] Step 2: Based on the acquired location information, calculate the location correlation coefficient between the non-direct transmission devices, and optimize the non-direct transmission devices by grouping them according to the size of the correlation coefficient, and group the non-direct transmission devices with small correlation coefficients into one group to obtain a plurality of non-direct transmission groups;

[0083] The UAV relay network is configured with a uniform linear array antenna, and the array steering vector of the uniform linear array is expressed as follows:

[0084]

[0085] Where λ is the wavelength, d a is the antenna element spacing, N U The number of uniform linear array antennas configured for the drone.

[0086] In step 2, based on the channel vector calculated from the position information, the position correlation coefficients of the i-th and j-th non-direct transmission devices are calculated as follows:

[0087]

[0088] in(·) T Indicates transpose.

[0089] The position correlation coefficients of all devices are obtained by pairwise calculation. In step 2, the GK non-direct transmission devices are divided into G groups, each group having K devices. Step 2 specifically includes the following steps:

[0090] Step 2-1: Calculate the correlation coefficient between the indirect transmission device and the indirect transmission group, randomly select a device from the GK indirect transmission devices and assign it to the first group, and calculate the correlation coefficient between any remaining unassigned device j and the known device group G i The position correlation coefficient of

[0091] Step 2-2: If the calculation result of the correlation system is less than the preset threshold value ρ th , that is, ij <ρ th , then assign the device j to the device group G i Otherwise, assign device j to a new empty set device group;

[0092] Step 2-3: Repeat steps 2-1 and 2-2 until all non-direct transmission devices are grouped.

[0093] Step 3: The UAV relay network uses a beamforming technology based on location information combined with a polling scheduling algorithm to serve a non-direct transmission group in each time slot to alleviate interference between groups and between devices within a group, thereby improving the overall communication quality;

[0094] The step 3 also includes establishing an optimization problem. In order to effectively alleviate the interference within the group while considering the fairness between devices, the optimization problem is expressed as follows:

[0095]

[0096] st||w k ||=1,k=1,2,…,K;

[0097] in, p k and p j represents the transmission power of the IoT device; w k represents the UAV receive beamforming weight vector; represents the variance of additive white Gaussian noise.

[0098] Based on the known location information of the non-direct transmission device in the drone relay network, and the drone receives the beamforming weight vector w k Independent of each other, the optimization problem can be transformed into:

[0099]

[0100] st||w k ||=1,k=1,2,…,K;

[0101] According to the generalized Rayleigh quotient, the optimized beamforming weight vector is calculated as follows:

[0102]

[0103] Step 4: The direct transmission device shares spectrum resources with the UAV and simultaneously accesses the low-orbit satellite network using a multi-beam non-orthogonal multiple access technology based on weighted beam superposition;

[0104] After the drone relay network successfully decodes the received signal of the non-direct transmission device, it forwards the re-encoded signal to the low-orbit satellite network, and the drone shares spectrum resources with multiple directly connected devices and accesses the low-orbit satellite network at the same time. In the tth time slot, the signal received by the low-orbit satellite network is expressed as:

[0105]

[0106] Among them, p s ,s∈{0,1,2,…,M} represents the transmission power of the UAV and the direct transmission device; v represents the receiving simulation beamforming weight vector of the low-orbit satellite network; h s represents the channel vector, n(t) represents additive white Gaussian noise;

[0107] In order to improve spectrum efficiency, the low-orbit satellite network is equipped with a phased array antenna, and adopts a multi-beam non-orthogonal multiple access technology based on weighted beam superposition to simultaneously receive signals sent by the M direct transmission devices and the drone;

[0108] The receive beam is the weighted sum of M+1 simulated beams, expressed as follows:

[0109] Uv=ω0v0+ω1v1…+ω m v m +…+ω M v M ;

[0110] Wherein, v represents the receiving analog beamforming weight vector of the low-orbit satellite network; U represents the coefficient matrix satisfying the constant modulus constraint of the phased array; ω s ,s∈{0,1,2,L,M} represents the weighting factor, satisfying ω0+ω1+L+ω m +L+ω M =1; and Respectively represent the receiving simulation beamforming weight vectors of the signals sent by the UAV and the mth direct transmission device, N S Indicates the number of phased array antennas configured in the low-orbit satellite network.

[0111] The coefficient matrix U includes a diagonal matrix, and the calculation of the diagonal elements of the diagonal matrix is ​​expressed as follows:

[0112]

[0113] in,

[0114] x=ω0cosb0+ω1cosb1+…+ω m cosb m +…+ω M cosb M ;

[0115] y=ω0sinb0+ω1sinb1+…+ω m sinb m +…+ω M sinb M ;

[0116] b s =(n-1)πcosθ s ,s∈{0,1,2,…,M};

[0117] The receive simulation beamforming weight vector v of the low-orbit satellite network is calculated as follows:

[0118]

[0119] in, acrcos(·) represents the arc cosine function.

[0120] According to the position, transmission power, antenna gain and other information of the UAV and the direct transmission device, the weighting factor ω is adaptively adjusted. s , s∈{0,1,2,L,M}, sort the channel quality of the UAV and the direct transmission device that need to access the low-orbit satellite network to meet x0→x1→L→x M The decoding order of

[0121] Therefore, the output signal-to-interference-noise ratios of the UAV and the mth direct transmission device obtained in the low-orbit satellite network are:

[0122]

[0123] The obtained output signal to interference and noise ratio is compared with a preset decoding threshold. If the output signal to interference and noise ratio is greater than the preset decoding threshold, it indicates that the signal is successfully transmitted, thereby meeting the heterogeneous service requirements of different types of devices in the low-orbit satellite Internet of Things.

[0124] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0125] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical scheme of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A hybrid multiple access method for low-orbit satellite Internet of Things, comprising a ground Internet of Things device and an access network, wherein the access network comprises a drone network and a low-orbit satellite network, wherein the ground Internet of Things device is configured with an omnidirectional antenna, wherein the ground Internet of Things device comprises a direct transmission device and a non-direct transmission device, wherein the drone network comprises a drone relay network and a drone, wherein the non-direct transmission device forwards a signal to the low-orbit satellite network through the drone relay network, wherein the direct transmission device shares spectrum resources with the drone and simultaneously accesses the low-orbit satellite network, wherein: The method comprises the following steps: Step 1: The low-orbit satellite network and the drone relay network obtain the location information of the ground IoT device through a location estimation method; Step 2: Based on the acquired location information, calculate the location correlation coefficient between the non-direct transmission devices, and optimize the non-direct transmission devices by grouping according to the size of the correlation coefficient to obtain a plurality of non-direct transmission groups; Step 3: The UAV relay network uses a beamforming technology based on location information combined with a polling scheduling algorithm to serve a non-direct transmission group in each time slot; Step 4: The direct transmission device shares spectrum resources with the UAV and simultaneously accesses the low-orbit satellite network using a multi-beam non-orthogonal multiple access technology based on weighted beam superposition.

2. A hybrid multiple access method applicable to low-orbit satellite Internet of Things according to claim 1, characterized in that: In the step 1, the position estimation method includes the Beidou satellite navigation system, the global navigation satellite system and the global positioning system; The position estimation method is expressed as follows: The location information of the i-th ground IoT device is expressed as (X i ,Y i ,0), the location information of the access network is expressed as (X l ,Y l ,H l ),l∈{s,u}; Wherein, s is the low-orbit satellite network, and u is the UAV relay network; Calculate the angle information of the i-th ground IoT device relative to the access network, where the angle information includes the angle of arrival, which is represented by θ i ; Based on the calculated angle information, the channel vector between the i-th terrestrial IoT device and the access network is expressed as follows: h i =L i b i a(θ i ); Among them, L i represents the path loss, which is calculated in dB as follows: Among them, d i represents the distance from the ith IoT device to the access network, and d i It is expressed as follows; Among them, β i Indicates small-scale fading. The UAV relay network channel obeys the Nakagami-m distribution, while the low-orbit satellite network channel obeys the shadow Rice distribution.

3. A hybrid multiple access method applicable to low-orbit satellite Internet of Things according to claim 2, characterized in that: The UAV relay network is configured with a uniform linear array antenna, and the array steering vector of the uniform linear array is expressed as follows: Where λ is the wavelength, d a is the antenna element spacing, N U The number of uniform linear array antennas configured for the drone.

4. A hybrid multiple access method applicable to low-orbit satellite Internet of Things according to claim 1, characterized in that: In step 2, based on the channel vector calculated from the position information, the position correlation coefficients of the i-th and j-th non-direct transmission devices are calculated as follows: in(·) T Indicates transpose.

5. A hybrid multiple access method applicable to low-orbit satellite Internet of Things according to claim 4, characterized in that: Divide the GK non-direct transmission devices into G groups, each group having K devices, and the step 2 specifically includes the following steps: Step 2-1: Calculate the correlation coefficient between the indirect transmission device and the indirect transmission group, randomly select a device from the GK indirect transmission devices and assign it to the first group, and calculate the correlation coefficient between any remaining unassigned device j and the known device group G i The position correlation coefficient of Step 2-2: If the calculation result of the correlation system is less than the preset threshold value ρ th , that is, ij <ρ th , then assign the device j to the device group G i Otherwise, assign device j to a new empty set device group; Step 2-3: Repeat steps 2-1 and 2-2 until all non-direct transmission devices are grouped.

6. A hybrid multiple access method applicable to low-orbit satellite Internet of Things according to claim 1, characterized in that: The step 3 also includes establishing an optimization problem, which is expressed as follows: s.t.||w k ||=1,k=1,2,…,K; in, p k and p j represents the transmission power of the IoT device; w k represents the UAV receive beamforming weight vector; represents the variance of additive white Gaussian noise.

7. A hybrid multiple access method applicable to low-orbit satellite Internet of Things according to claim 6, characterized in that: Based on the known location information of the non-direct transmission device in the drone relay network, and the drone receives the beamforming weight vector w k Independent of each other, the optimization problem can be transformed into: s.t.||w k ||=1,k=1,2,…,K; According to the generalized Rayleigh quotient, the optimized beamforming weight vector is calculated as follows:

8. The hybrid multiple access method applicable to low-orbit satellite Internet of Things according to claim 1, characterized in that: In step 4, after the drone relay network successfully decodes the received signal of the non-direct transmission device, it forwards the re-encoded signal to the low-orbit satellite network, and the drone shares spectrum resources with multiple directly connected devices and accesses the low-orbit satellite network at the same time. In the tth time slot, the signal received by the low-orbit satellite network is expressed as: Among them, p s ,s∈{0,1,2,…,M} represents the transmission power of the UAV and the direct transmission device; v represents the receiving simulation beamforming weight vector of the low-orbit satellite network; h s represents the channel vector, n(t) represents additive white Gaussian noise; The low-orbit satellite network is configured with a phased array antenna, and uses a multi-beam non-orthogonal multiple access technology based on weighted beam superposition to simultaneously receive signals sent from the M direct transmission devices and the drone; The receive beam is the weighted sum of M+1 simulated beams, expressed as follows: Uv=ω0v0+ω1v1…+ω m v m +…+oh M v M ; Wherein, v represents the receiving analog beamforming weight vector of the low-orbit satellite network; U represents the coefficient matrix satisfying the constant modulus constraint of the phased array; ω s ,s∈{0,1,2,L,M} represents the weighting factor, satisfying ω0+ω1+L+ω m +L+ω M =1; and Respectively represent the receiving simulation beamforming weight vectors of the signals sent by the UAV and the mth direct transmission device, N S Indicates the number of phased array antennas configured in the low-orbit satellite network.

9. A hybrid multiple access method applicable to low-orbit satellite Internet of Things according to claim 8, characterized in that: The coefficient matrix U includes a diagonal matrix, and the calculation of the diagonal elements of the diagonal matrix is ​​expressed as follows: in, x=ω0 cos b0+ω1 cos b1+…+ω m cos b m +…+oh M cosb M ; y=ω0 sin b0+ω1 sin b1+…+ω m sin b m +…+oh M sin b M ; b s =(n-1)πcosθ s ,s∈{0,1,2,…,M}; The receive simulation beamforming weight vector v of the low-orbit satellite network is calculated as follows: in, acrcos(·) represents the arc cosine function.

10. A hybrid multiple access method applicable to low-orbit satellite Internet of Things according to claim 8, characterized in that: By adjusting the weighting factor ω s , s∈{0,1,2,L,M}, sort the channel quality of the UAV and the direct transmission device that need to access the low-orbit satellite network to meet x0→x1→L→x M The decoding order of Therefore, the output signal-to-interference-noise ratios of the UAV and the mth direct transmission device obtained in the low-orbit satellite network are: The obtained output signal to interference plus noise ratio is compared with a preset decoding threshold, and if the output signal to interference plus noise ratio is greater than the preset decoding threshold, it indicates that the signal is successfully transmitted.

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