Satellite beam scanning scheduling method and device for preventing same-frequency interference and storage medium

Abstract

This application discloses a satellite beam scanning scheduling method, apparatus, and storage medium for preventing co-channel interference. Belonging to the field of satellite communications, the method includes: determining sub-frequency bands adapted to multiple beams of the satellite; initializing a particle swarm, wherein each element of the particle vector of the particle swarm indicates the wave positions scanned by each beam of the satellite in each hop beam time slot of the hop beam period through different numerical ranges; constructing an adaptation function, wherein the function value of the adaptation function is related to the severity of co-channel interference within the satellite coverage area; iteratively optimizing the particle swarm according to a particle swarm optimization algorithm to determine the optimal particle vector; and determining the wave positions scanned by each beam in each hop beam time slot of the hop beam period based on the optimal particle vector. Thus, this application effectively avoids coverage overlap and signal cross-interference between adjacent co-channel sub-beams by optimizing the wave position scanning scheduling scheme within the hop beam period.

Description

Technical Field

[0001] This application relates to the field of satellite communication technology, and in particular to a satellite beam scanning scheduling method, apparatus and storage medium for preventing co-channel interference. Background Technology

[0002] In the field of modern satellite communications, to maximize single-satellite coverage and spectrum utilization efficiency, high-throughput communication satellites and low-Earth orbit satellites generally employ multi-beam scanning technology. Through phased array antennas or reflector feed systems, 8-16 or even more independent beams (denoted as B1~B1) can be generated on a single satellite. m (e.g., the number of beams per Starlink satellite). Each beam is precisely pointed to a specific geographical area on the ground, and different beams can reuse the same frequency bands such as Ku and Ka. With the help of spatial division multiplexing (SDMA) technology, the capacity limitations of traditional single-beam satellites are greatly broken, and the capacity of satellite systems is multiplied.

[0003] For example, publication number CN114938515A, entitled "A Method and System for Fault Detection in Satellite Multibeam Multiplexing Networks," provides a method and system for fault detection in satellite multibeam multiplexing networks, relating to the field of satellite communication technology. The method includes: S1, identifying the satellite link node as a suspected fault point, initiating a collaborative task, and initializing it; S2, decomposing the collaborative task into a series of atomic actions and synchronizing them; S3, generating the theoretical and actual topology of the management domain, retrieving all satellite link nodes that can theoretically and practically communicate with each interface of the suspected fault point, and selecting the interface corresponding to the suspected fault point; S4, the sub-management station sends the collaborative task to each collaborative agent and reports the collaborative results to the sub-management station; S5, the network management center generates the theoretical and actual global topology of the satellite network, and selects the best collaborative satellite link fault node corresponding to the interface of the suspected fault point; S6, the network management center sends the collaborative task, and after receiving the collaborative results, forwards them to the sub-management station to determine the fault type.

[0004] For example, publication number CN103796319A, entitled "A Method for Downlink Frequency Reuse in Multi-Beam Satellite Mobile Communication," discloses a method for downlink frequency reuse in a multi-beam satellite mobile communication system. This method involves: 1) Dividing users in the downlink of the multi-beam satellite communication system into N groups, each group containing K users from different beams, with users in the same group satisfying the maximum user spacing; K is the total number of beams in the multi-beam satellite communication system; 2) Allocating a different frequency point to each group of users, with each group transmitting downlink signals on its allocated frequency point; 3) The ground control station monitors the signal-to-interference-plus-noise ratio (SNR) of the received signals of users; if the SNR of a user's received signal is less than a set threshold, a reserved frequency point is allocated to that user for downlink signal transmission; otherwise, the user precodes the transmitted signal using the allocated frequency point before transmission. This invention solves the adaptability problem of precoding interference cancellation methods in satellite communication systems, effectively improving the system's frequency utilization and system capacity.

[0005] However, due to limitations in antenna hardware design and physical propagation characteristics, satellite-generated beams cannot achieve the ideal "needle beam" shape, inevitably resulting in sidelobe signal leakage and beam roll-off. When the coverage areas of two beams reusing the same frequency band overlap (especially in areas with edge users), the receiver will simultaneously receive the useful signal of the target beam and the interference signal of the adjacent beam, leading to a significant decrease in the signal-to-interference-plus-noise ratio (SINR), severely impacting communication quality and data transmission rate. Furthermore, to further improve spectrum utilization, the industry often adopts denser high-frequency multiplexing strategies (such as 4-color multiplexing compared to 7-color multiplexing, resulting in shorter beam spacing between beams at the same frequency). While this strategy improves frequency band reuse efficiency, it further exacerbates interference problems between adjacent beams at the same frequency.

[0006] In existing technologies, multi-color multiplexing is used for the frequency bands allocated to satellites, such as four-color multiplexing (seven-color multiplexing is also possible). This involves dividing the frequency bands allocated to satellites into non-overlapping sub-bands, from which the satellite selects appropriate sub-bands for each beam. However, in practical applications, the environmental conditions of each beam's coverage area objectively require matching it with the most suitable sub-band. Therefore, adjacent beams may still objectively use the same sub-band for scanning.

[0007] The existing technologies mentioned above do not adequately consider the objective requirements of environmental differences in the coverage areas of each beam for adapting sub-frequency bands, which leads to adjacent beams still using the same sub-frequency band for scanning in practical applications, thus failing to effectively solve the technical problem of co-channel interference. No effective solution has been proposed yet. Summary of the Invention

[0008] The embodiments of this disclosure provide a satellite beam scanning scheduling method, apparatus, and storage medium to prevent co-channel interference, thereby at least addressing the technical problem in the prior art that the objective requirements for adapting sub-frequency bands due to insufficient consideration of the environmental differences in the coverage areas of each beam are not taken into account, resulting in adjacent beams still using the same sub-frequency band for scanning in practical applications, and thus failing to effectively solve the co-channel interference problem.

[0009] According to one aspect of the present disclosure, a satellite beam scanning scheduling method for preventing co-channel interference is provided, comprising: determining sub-frequency bands adapted to multiple beams of a satellite respectively; initializing a particle swarm, wherein each element of the particle vector of the particle swarm indicates the wave positions scanned by each beam of the satellite in each hop beam time slot of a hop beam period through different numerical ranges; constructing an adaptation function, wherein the function value of the adaptation function is related to the severity of co-channel interference within the satellite coverage area; iteratively optimizing the particle swarm according to a particle swarm optimization algorithm to determine an optimal particle vector; and determining the wave positions scanned by each beam in each hop beam time slot of a hop beam period based on the optimal particle vector.

[0010] According to another aspect of the present disclosure, a storage medium is also provided, the storage medium including a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0011] According to another aspect of the present disclosure, a satellite beam scanning scheduling device for preventing co-channel interference is also provided, comprising: an adaptation module for determining sub-frequency bands adapted to multiple beams of a satellite respectively; an initialization module for initializing a particle swarm, wherein each element of the particle vector of the particle swarm indicates the wave position scanned by each beam of the satellite in each hop beam time slot of the hop beam period through different numerical ranges; a construction module for constructing an adaptation function, wherein the function value of the adaptation function is related to the severity of co-channel interference within the satellite coverage area; an iteration module for iteratively optimizing the particle swarm according to a particle swarm optimization algorithm to determine the optimal particle vector; and a determination module for determining the wave position scanned by each beam in each hop beam time slot of the hop beam period based on the optimal particle vector.

[0012] According to another aspect of the present disclosure, a satellite beam scanning scheduling device for preventing co-channel interference is also provided, comprising: a processor; and a memory connected to the processor, configured to provide the processor with instructions for processing the following steps: determining sub-frequency bands adapted to multiple beams of a satellite respectively; initializing a particle swarm, wherein each element of the particle vector of the particle swarm indicates the wave positions scanned by each beam of the satellite in each hop beam time slot of a hop beam period through different numerical ranges; constructing an adaptation function, wherein the function value of the adaptation function is related to the severity of co-channel interference within the satellite coverage area; iteratively optimizing the particle swarm according to a particle swarm optimization algorithm to determine an optimal particle vector; and determining the wave positions scanned by each beam in each hop beam time slot of a hop beam period based on the optimal particle vector.

[0013] In a satellite communication scenario, this application first selects a specific sub-frequency band most suitable for the coverage area of ​​each beam generated by the satellite. Next, it performs particle swarm optimization (PSO) initialization, where the values ​​of each particle vector element are limited to a preset range for the corresponding beam and are randomly generated, forming an initial set of candidate scanning strategies. Then, it constructs an adaptation function to convert co-channel interference within the satellite coverage area into quantified values, providing a basis for evaluating the scheme. Subsequently, using the initialized particle vectors and adaptation function, iterative calculations are performed using a particle swarm optimization algorithm. Based on the particle's own historical optimal solution and the global optimal solution of the swarm, the particle's movement direction and step size are adjusted to generate a new vector, determining the optimal particle vector. Finally, the position and value of each element are analyzed to determine the optimal PSO vector for each beam (B1~B2). m By scanning wavelets in each time slot of the hopping beam cycle, a complete wavelet scanning scheme is formed to minimize co-channel interference in the satellite coverage area and ensure communication quality. Therefore, this application, while meeting the objective requirements of each beam's coverage area for adaptable sub-frequency bands, effectively avoids coverage overlap and signal cross-interference between adjacent co-frequency sub-beams by optimizing the wavelet scanning scheduling scheme within the hopping beam cycle. This solves the technical problem in existing technologies where the objective requirements of environmental differences in each beam's coverage area for adaptable sub-frequency bands are not fully considered, leading to adjacent beams potentially using the same sub-frequency band for scanning in practical applications, thus failing to effectively solve co-channel interference. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of this disclosure and form part of this application, illustrate exemplary embodiments of this disclosure and are used to explain this disclosure, but do not constitute an undue limitation of this disclosure. In the drawings:

[0015] Figure 1 This is a hardware structure block diagram of a computing device for implementing the method described in Embodiment 1 of this disclosure;

[0016] Figure 2This is a flowchart illustrating a satellite beam scanning scheduling method for preventing co-channel interference according to Embodiment 1 of this disclosure;

[0017] Figure 3 This is a schematic diagram of the satellite beam coverage area of ​​a satellite beam scanning and scheduling method for preventing co-channel interference according to Embodiment 1 of this disclosure;

[0018] Figure 4 This is a schematic diagram of the structure of a single satellite coverage area and its corresponding wavelet cluster in a satellite beam scanning scheduling method for preventing co-channel interference according to Embodiment 1 of this disclosure;

[0019] Figure 5 This is a schematic diagram of the overlapping area of ​​two beams from the same frequency band, according to a satellite beam scanning scheduling method for preventing co-channel interference as described in Embodiment 1 of this disclosure.

[0020] Figure 6 This is a schematic diagram of the overlapping area of ​​two beams from the same frequency band, according to a satellite beam scanning scheduling method for preventing co-channel interference as described in Embodiment 1 of this disclosure.

[0021] Figure 7 This is a schematic diagram of the iterative process of the particle swarm optimization algorithm for a satellite beam scanning scheduling method to prevent co-channel interference as described in Embodiment 1 of this disclosure;

[0022] Figure 8 This is a schematic diagram of a neural network model of a satellite beam scanning scheduling method for preventing co-channel interference according to Embodiment 1 of this disclosure;

[0023] Figure 9 This is a schematic diagram of a satellite beam scanning and scheduling device for preventing co-channel interference according to Embodiment 2 of this disclosure;

[0024] Figure 10 This is a schematic diagram of a satellite beam scanning and scheduling device for preventing co-channel interference, as described in Embodiment 3 of this disclosure. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] According to this embodiment, a satellite beam scanning scheduling method for preventing co-channel interference is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1 This is a schematic diagram of the hardware architecture of Satellite-110. (Reference) Figure 1 As shown, satellite 110 includes an integrated electronic system, which comprises a processor, a memory, a bus management module, and a communication interface. The memory is connected to the processor, allowing the processor to access the memory, read program instructions stored in the memory, read data from the memory, or write data to the memory. The bus management module is connected to the processor and also to a bus such as a CAN bus. Thus, the processor can communicate with onboard peripherals connected to the bus through the bus managed by the bus management module. These onboard peripherals include: onboard peripheral 1 (GNSS module), onboard peripheral 2 (fiber optic gyroscope), ..., onboard peripheral n (torque flywheel). Furthermore, the processor also communicates with devices such as cameras, star sensors, telemetry and control transponders, and data transmission equipment via the communication interface. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, a satellite system may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] It should be noted that, Figure 1One or more processors and / or other data processing circuits shown herein may generally be referred to as "data processing circuitry". This data processing circuitry may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be integrated, in whole or in part, into any other element in a computing device. As involved in embodiments of this disclosure, the data processing circuitry serves as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0031] Figure 1 The memory shown can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for determining the communication frequency band corresponding to the beam in the embodiments of this disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, to implement the above-mentioned method for determining the communication frequency band corresponding to the beam in the application program. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0032] It should be noted here that, in some optional embodiments, the above... Figure 1 The device shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned devices.

[0033] Under the aforementioned operating environment, according to the first aspect of this embodiment, a satellite beam scanning scheduling method for preventing co-channel interference is provided. This method comprises... Figure 1 The processor implementation shown. Figure 2 A flowchart illustrating the method is shown below. (Refer to...) Figure 2 As shown, the method includes:

[0034] S202: Determine the sub-bands that are adapted to the multiple beams of the satellite respectively;

[0035] S204: Initialize the particle swarm, where each element of the particle vector of the particle swarm indicates the wave position scanned by each beam of the satellite in each hop beam time slot of the hop beam period through different numerical ranges.

[0036] S206: Construct an adaptation function, where the function value of the adaptation function is related to the severity of co-channel interference within the satellite coverage area;

[0037] S208: Iteratively optimize the particle swarm using the particle swarm optimization algorithm to determine the optimal particle vector; and

[0038] S210: Based on the optimal particle vector, determine the wave position scanned by each beam in each hop beam time slot of the hop beam period.

[0039] Specifically, in a satellite communication scenario, the processor needs to select the most suitable sub-frequency band for each beam generated by satellite 110 from multiple sub-frequency bands available to satellite 110 for its coverage area (corresponding to step S202). That is, in this embodiment of the invention, the processor can divide the frequency band allocated to satellite 110 into four non-overlapping sub-frequency bands N1~N4, so that satellite 110 can select suitable sub-frequency bands from sub-frequency bands N1~N4 and allocate them to each beam B1~B1. m Among them, the frequencies used in sub-bands N1 to N4 increase sequentially.

[0040] It should be noted that the reference Figure 3 As shown, satellite 110 can use m beams B1~B1 respectively. m Simultaneously covering multiple coverage areas S1~S m For example, beam B1 covers area S1, which contains corresponding wave positions P1 to P3. Beam B1 is used to scan wave positions P1 to P3 and can provide satellite communication services to the covered wave positions P1 to P3. Beam B2 covers area S2, which contains corresponding wave positions P4 to P6. Beam B2 is used to scan wave positions P4 to P6 and can provide satellite communication services to the covered wave positions P4 to P6; ...; beam B m The covered area is S m Coverage area S m The inner corresponding wave position P 3m-2 ~P 3m Thus, beam B m Used for wave position P 3m-2 ~P 3m Perform a scan, and beam B m It can be directed to the covered wave position P 3m-2 ~P 3m It provides satellite communication services. Thus, satellite 110 can switch between corresponding positions using m beams and provide satellite communication services to each corresponding position in a time-division manner.

[0041] Furthermore, in a satellite multi-beam communication system, to optimize the beam position scanning scheme within each beam hopping period, the processor needs to perform a particle swarm initialization operation. The particle swarm is a set of candidate schemes used to search for the optimal beam position scanning strategy. Each particle corresponds to a complete beam position scanning strategy, and the strategy information is carried in the form of a particle vector. The dimension of the particle vector is determined by the number of beams in satellite 110 and the number of hopping beam slots included in a single hopping beam period. That is, the total number of elements in the particle vector is equal to the product of the number of beams and the number of hopping beam slots. Each element corresponds to the beam position selection result of a specific beam within a specific hopping beam slot (corresponding to step S204).

[0042] In embodiments of the present invention, to clarify the correspondence between each element and the wave position, the processor needs to pre-configure a dedicated numerical range for each beam of satellite 110. The numerical ranges corresponding to different beams do not overlap, and any value within each numerical range is uniquely mapped to a wave position that the beam can scan. For example, if satellite 110 includes two beams, beam B1 and beam B2, and the hopping beam period includes two hopping beam time slots, T1 and T2, then the particle vector includes four elements, corresponding to the wave positions of "beam B1 in hopping beam time slot T1", "beam B1 in hopping beam time slot T2", "beam B2 in hopping beam time slot T1", and "beam B2 in hopping beam time slot T2", respectively; at the same time, the numerical range corresponding to the wave position of beam B1 is pre-set to [1, 10] (value 1 corresponds to wave position P). 11 Value 2 corresponds to wave position P 12 ...The value 10 corresponds to wave position P. 110 The wave position of beam B2 corresponds to the numerical range [11, 20] (the value 11 corresponds to wave position P). 21 The value 12 corresponds to wave position P. 22 ...The value 20 corresponds to wave position P. 210 ).

[0043] Next, an adaptation function is constructed to transform the severity of co-channel interference within the satellite coverage area into a quantifiable and comparable function value, providing a core basis for evaluating the merits of candidate wavelet scanning schemes (i.e., the schemes corresponding to each particle in the particle swarm) (corresponding to step S206). Specifically, when beams corresponding to two coverage areas are assigned to the same sub-frequency band, and the two coverage areas overlap, or signal crossover occurs due to beam sidelobe leakage, co-channel interference will occur. The adaptation function transforms this co-channel interference into a calculable quantifiable value. The more overlapping areas of the co-channel sub-beams, the larger the interference quantification value output by the adaptation function, indicating that the co-channel interference problem of the sub-frequency band allocation scheme is more severe. Conversely, the fewer overlapping areas of the co-channel sub-beams, the smaller the interference quantification value output by the adaptation function, indicating that the interference control effect of the sub-frequency band allocation scheme is better. This provides a clear and quantifiable judgment standard for the subsequent particle swarm optimization algorithm to select the optimal sub-frequency band allocation scheme.

[0044] Furthermore, using the initialized particle vectors X1~X n The optimal particle vector is determined iteratively using the particle swarm optimization algorithm, along with the fitness function S(X). (Corresponding to step S208) n The algorithm evaluates the performance of each particle in the initial particle swarm and the wave position scanning strategy it represents. Then, in each iteration, based on the particle's own historical optimal solution (i.e., the particle vector corresponding to the optimal fitness function value experienced by the particle) and the swarm's global optimal solution (i.e., the particle vector corresponding to the optimal fitness function value among all current particles), it adjusts the movement direction and step size of each particle according to a preset velocity update formula, generating new particle vectors. The fitness function value of the newly generated particle vectors is recalculated, updating the particle's own historical optimal solution and the swarm's global optimal solution, and the above iteration process is repeated. When the number of iterations reaches a preset threshold, or the fitness function value of the swarm's global optimal solution tends to stabilize (i.e., the change is less than a preset precision), the iteration stops. The particle vector corresponding to the swarm's global optimal solution at this point is the optimal particle vector, and the wave position scanning strategy indicated by this particle vector is the optimized solution that minimizes co-frequency interference.

[0045] Finally, by analyzing the position and value of each element in the optimal particle vector, the positions of each beam B1~B1 in each hopping beam time slot of the hopping beam period are determined. m The scanning wave position (corresponding to step S210). Specifically, the elements corresponding to beam B1 in each hop beam time slot are obtained from the optimal particle vector. Based on the element values ​​and the preset numerical-wave position mapping rule, the scanning wave position of beam B1 in each hop beam time slot is determined. Similarly, the elements corresponding to beams B2 to B1 in the optimal particle vector are obtained sequentially. mBy determining the scanning positions of each beam in different hopping beam time slots, corresponding elements are used to form a complete scanning scheme for each beam throughout the entire hopping beam period. This scheme can minimize co-channel interference within the satellite coverage area and ensure communication quality.

[0046] Therefore, the determined beams B1~B m Within the hopping beam cycle, the scanning positions of each hopping beam time slot are converted into beam control commands executable by Satellite 110. Satellite 110 then drives the phased array antenna or reflector feed system according to these commands, precisely pointing each beam to the preset position for scanning in the corresponding time slot. This effectively avoids beam overlap interference from adjacent co-frequency beams in the same time slot, significantly reduces co-frequency interference levels within the satellite's coverage area, improves the signal-to-interference-plus-noise ratio and communication quality, and ultimately optimizes the spectrum utilization efficiency and service performance of the satellite multi-beam communication system.

[0047] As described in the background section above, in the field of modern satellite communications, high-throughput communication satellites and low-Earth orbit satellites generally employ multi-beam scanning technology using phased array antennas or reflector feed systems to maximize single-satellite coverage and spectrum utilization efficiency. A single satellite can generate 8-16 or even more independent beams, each pointing to a specific area on the ground and reusing the same frequency bands such as Ku and Ka. Spatial division multiplexing technology has significantly broken through the capacity limitations of traditional single-beam satellites, achieving a multiple increase in system capacity. However, due to limitations in antenna hardware design and physical propagation characteristics, beams suffer from sidelobe signal leakage and edge roll-off. When beams of the same frequency overlap, it leads to a decrease in the signal-to-interference-plus-noise ratio at the receiver, and denser high-frequency spectrum reuse strategies will further exacerbate this interference problem. Although existing technologies use multi-color multiplexing (such as four-color or seven-color multiplexing) to divide the satellite frequency band into non-overlapping sub-bands and allocate them to each beam to alleviate interference, in practical applications, because the environment of each beam's coverage area needs to be matched with the most suitable sub-band, adjacent beams may still use the same sub-band for scanning.

[0048] In view of this, in the context of satellite communication, this application first selects a specific sub-frequency band most suitable for the coverage area of ​​each beam generated by the satellite; then, it performs a particle swarm initialization operation, during which the values ​​of each particle vector element are limited to a preset value range for the corresponding beam and are randomly generated to form an initial candidate scanning strategy set; subsequently, it constructs an adaptation function to convert co-channel interference within the satellite coverage area into quantified values, providing a basis for evaluating the scheme; then, using the initialized particle vectors and adaptation function, it iterative calculations are performed through a particle swarm optimization algorithm, adjusting the particle movement direction and step size based on the particle's own historical optimal solution and the global optimal solution of the swarm to generate a new vector, thus determining the optimal particle vector; finally, it analyzes the position and value of each element to determine the values ​​of each beam B1~B1. mBy scanning wavelets in each time slot of the hopping beam cycle, a complete wavelet scanning scheme is formed to minimize co-channel interference in the satellite coverage area and ensure communication quality. Therefore, this application, while meeting the objective requirements of each beam's coverage area for adaptable sub-frequency bands, effectively avoids coverage overlap and signal cross-interference between adjacent co-frequency sub-beams by optimizing the wavelet scanning scheduling scheme within the hopping beam cycle. This solves the technical problem in existing technologies where the objective requirements of environmental differences in each beam's coverage area for adaptable sub-frequency bands are not fully considered, leading to adjacent beams potentially using the same sub-frequency band for scanning in practical applications, thus failing to effectively solve co-channel interference.

[0049] Optionally, the operation of initializing the particle swarm includes: generating multiple particle vectors; randomly encoding the elements of each particle vector according to pre-set constraints to determine the wave positions corresponding to different elements in each particle vector; and randomly sampling within the corresponding numerical range according to the wave positions corresponding to different elements in each particle vector to determine the values ​​of different elements in each particle vector, wherein the constraints stipulate that multiple beams scan all their corresponding wave positions within the beam-hopping period.

[0050] Specifically, to achieve precise control of co-channel interference in the satellite coverage area, this application employs a particle swarm optimization algorithm to target the satellite-generated beams B1~B1. m This involves determining the specific scheduling strategy for scanning the corresponding positions in each hop beam time slot within a hop beam cycle. Each beam's coverage area corresponds to a set of preset positions (i.e., position clusters), with the coverage area S... i Taking the corresponding wavelet cluster as an example (e.g.) Figure 4 As shown), in an embodiment of the present invention, the wavelet cluster includes 7 wavelets, denoted as wavelet 1 to wavelet 7, and beam B. i A full coverage scan of these 7 positions needs to be completed within the hopping beam cycle. Based on this, in each hopping beam time slot, beam B... i Only one of the wave positions 1 through 7 is scanned. That is, a hopping beam cycle must include at least 7 hopping beam slots to ensure beam B... i Traversing all beam positions to achieve complete coverage of the coverage area; for the sake of simplicity, the embodiments of the present invention are all illustrated using a scenario in which one hopping beam cycle includes 7 hopping beam time slots as an example.

[0051] Specifically, in order to accurately characterize beams B1~B m The beam-position scanning strategy within the beam-hopping period first defines the particle vector X=[x1, x2, ..., x...]. 7m ] T Wherein, each element x in the particle vector X L(L=1,2,...,7m), its value can be any real number from 0 to 7, and this value directly corresponds to the wave position number to be scanned by the beam within a specific hopping beam time slot. Specifically:

[0052] If 0 ≤ x L If ≤1, then the corresponding wave position is wave position 1;

[0053] If 1 < x L If ≤2, then the corresponding wave position is wave position 2;

[0054] If 2 < x L If ≤3, then the corresponding wave position is wave position 3;

[0055] If 3 < x L If the value is ≤4, then the corresponding wave position is wave position 4;

[0056] If 4 < x L If ≤5, then the corresponding wave position is wave position 5;

[0057] If 5 < x L If ≤6, then the corresponding wave position is wave position 6; and

[0058] If 6 < x L If ≤7, then the corresponding wave position is wave position 7.

[0059] Furthermore, x1~x m Used to indicate the first hop beam time slot, each beam B1~B m The scanned wave position;

[0060] Where x m+1 ~x 2m Used to indicate the second hop beam time slot, each beam B1~B m The scanned wave position; ......;

[0062] And so on, x 6m+1 ~x 7m Used to indicate the 7th hop beam time slot, each beam B1~B m The wave position being scanned.

[0063] Then, the constraint is defined as follows: within a hopping beam cycle, each beam must scan at least all of its respective positions. Multiple particle vectors X1~X are then randomly determined according to this constraint. n The wave positions corresponding to each element specifically include:

[0064] Generate multiple particle vectors X1~X n ,in

[0065] X k= [xk,1 , x k,2 , x k,3 , ..., x k,7m ] T

[0066] Based on this constraint, the elements of each particle vector are randomly encoded to determine the wave positions corresponding to different elements in each particle vector. This ensures that the wave positions mapped to the elements corresponding to each beam completely include all wave positions of that beam, avoiding any omissions. For example, for each particle vector X... k Randomly determine each element x k,1 ~x k,7m The corresponding wave position. For example, determining x. k,1 Corresponding wave position 1, x k,2 Corresponding wave positions 2, ..., x k,7m Corresponding wave position 5.

[0067] Based on the wave position corresponding to different elements of each particle vector, random sampling is performed within the corresponding numerical range to determine the values ​​of different elements of each particle vector, thereby realizing the analysis of the particle swarm X0~X n Initialization.

[0068] For example, for particle vector X k Because of x k,1 Corresponding to wave position 1, therefore, random sampling is performed in the interval [0,1] to determine x. k,1 The value of x; because x k,2 Corresponding to wave position 2, therefore, random sampling is performed in the interval (1,2) to determine x. k,2 The value of x; ...; due to x k,7m Corresponding to wave position 5, therefore, random sampling is performed in the interval (4,5) to determine x. k,7m The value.

[0069] Therefore, for particle vectors X1~X n Perform initialization. And set the values ​​for each particle from X0 to X... n initial velocity v0~v n The initial velocity value needs to be set within a reasonable range based on the wave position value range and the optimization step size requirements, so as to provide a basis for the subsequent iterative updates of the particle swarm optimization algorithm and finally complete the initialization preparation of the entire particle swarm.

[0070] By employing the above methods, the coverage blind spot problem caused by missing wave positions is avoided. At the same time, randomized encoding and numerical sampling provide a diverse and sufficient set of initial candidate schemes for the particle swarm optimization algorithm. Combined with the preset initial velocity, this lays a reliable foundation for subsequent iterative search to select the optimal wave position scanning strategy that can avoid co-frequency interference, ensuring that the optimization process can both cover the effective search space and converge efficiently to the optimal solution.

[0071] Optionally, the operation of constructing the fitness function includes constructing a fitness function S(X) as follows:

[0072] (1)

[0073] Where I1(X) represents the time slots of each beam B1~B according to the particle vector X in each beam skipping beam slot. m In the case of beam allocation, the number of overlapping regions formed by two beams of the same frequency band appearing throughout the entire beam-hopping period; and I2(X) represents the number of beams B1~B1 in each beam-hopping time slot according to the particle vector X. m In the case of assigned beam positions, the number of overlapping regions formed by the overlap of three beams in the same frequency band that occur throughout the entire hopping beam period.

[0074] Specifically, an adaptation function S(X) is defined, which reflects the severity of co-channel interference within the satellite coverage area:

[0075] Wherein, I1(X) represents the values ​​of each element of the particle vector X within each hopping beam time slot for each beam B1~B. m In the case of beam allocation, the number of overlapping regions that occur in the entire hop beam period due to the overlap of two beams of the same frequency band (i.e., the sum of the number of overlapping regions that occur in each hop beam time slot due to the overlap of two beams of the same frequency band).

[0076] refer to Figure 5 As shown, the coverage areas of beams B1 and B2 overlap. Therefore, when beams B1 and B2 use the same sub-frequency band, two types of co-channel interference will occur in the overlapping area SD1: beam B2 will interfere with users establishing communication via beam B1; and beam B1 will interfere with users establishing communication via beam B2. Therefore, taking... This is reflected in the numerical range of each element of the particle vector X in each hopping beam time slot as beams B1~B1. m The severity of co-channel interference caused by the overlapping area of ​​two co-channel beams when the wavelength is allocated.

[0077] Furthermore, I2(X) represents the values ​​of each element of the particle vector X within each hopping beam time slot for each beam B1~B. m In the case of beam allocation, the number of overlapping regions that occur in the entire hop beam period, which are the overlap of three beams of the same frequency band (i.e., the sum of the number of overlapping regions that occur in each hop beam time slot, which are the overlap of three beams of the same frequency band).

[0078] refer to Figure 6 As shown, the coverage areas of beams B1 to B3 overlap. Therefore, when beams B1 to B3 use the same sub-frequency band, six types of co-channel interference will occur in the overlapping area SD2: beams B2 and B3 interfere with users establishing communication via beam B1; beams B1 and B3 interfere with users establishing communication via beam B2; and beams B1 and B2 interfere with users establishing communication via beam B3. Therefore, taking... This is reflected in the time slots of each hopping beam, where the particle vector X corresponds to beams B1~B1. m Given a beam assignment, S(X) represents the severity of co-channel interference caused by the overlapping area of ​​three beams in the same frequency band. Therefore, S(X) as a whole reflects the severity of co-channel interference within the satellite's coverage area.

[0079] By constructing a weighted form of the adaptive function, the precise quantification and efficient optimization of satellite multi-beam co-frequency interference are achieved. This promotes the rapid aggregation of the particle swarm towards the low-interference sub-frequency band allocation scheme, enabling the algorithm to converge efficiently to the optimal solution. Ultimately, this significantly enhances the suppression effect on satellite multi-beam co-frequency interference, ensuring communication quality and spectrum utilization efficiency.

[0080] Optionally, the iterative optimization of the particle swarm according to the particle swarm optimization algorithm includes updating each particle and its corresponding velocity coefficient according to the following formula:

[0081] (2)

[0082] （3）

[0083] Where X k t and V k t Let X and Y represent the particle determined in the t-th iteration, respectively. k and velocity coefficient V k ;X k t+1 and V k t+1 Let X and Y represent the particle determined in the (t+1)th iteration, respectively. k and velocity coefficient V kXbest k t For particle X k The individual optimal solution determined in the t-th iteration; Xbest t is the global optimal solution determined by the particle swarm in the t-th iteration; w is the inertia factor, which takes a non-negative value; c1 and c2 are learning factors, used to adjust the convergence speed of the algorithm; and r1 and r2 are random numbers between 0 and 1.

[0084] Specifically, Figure 7 This is a schematic diagram of the iterative process of the particle swarm optimization algorithm, for reference. Figure 7 As shown:

[0085] First, based on the randomly generated particle pairs Xbest k Xbest, V k Perform initialization (corresponding to step S702); then, update each particle X using formulas (2) and (3). k and velocity coefficient V k (Corresponding to step S704); then, the updated particles are selected according to the constraints (corresponding to step S706); afterwards, based on the selected particle X... k Update the optimal function value Fbest for each particle. k And the overall optimal function value Fbest of the particle swarm (corresponding to step S708); then determine whether the iteration termination condition is met (corresponding to step S710). If it is not met (N), return to step S704 to continue. If it is met (Y), execute step S712 and output Xbest. The entire process gradually approaches the optimal solution through iterative optimization.

[0086] It should be noted that when initializing the particle swarm, the values ​​of each element of the particle vector for each particle must be strictly limited to the preset value range of the corresponding beam. The specific values ​​of each element are determined by random generation, so that each particle vector completely and clearly indicates the wave position scanned by each hop beam time slot within the hop beam period, forming an initial set of candidate scanning strategies, which provides a basis for the subsequent optimization search process.

[0087] By employing the above methods, the particle swarm is ensured to possess both inertia and learning capabilities during the scanning process (approaching individual and global optimality), while constraint screening and iterative loops prevent deviation from the target. This efficiently finds the optimal beam scanning strategy that minimizes co-frequency interference from multiple satellite beams, providing reliable algorithmic support for the subsequent determination of beam scanning schemes.

[0088] Optionally, the operation of determining the wave position scanned by each beam in each hopping beam slot of the hopping beam period according to the optimal particle vector includes: determining the wave position corresponding to the value of each element in the optimal particle vector; and determining the wave position of each beam in each hopping beam slot according to the beam corresponding to each element in the optimal particle vector and the hopping beam slot.

[0089] Specifically, based on the optimized particle vector Xbest, the values ​​of each beam B1~B in each hopping beam time slot of the hopping beam period are determined. m The scanned wave positions. Based on the values ​​of the elements in Xbest corresponding to each wave position in each hop beam slot, the scanned wave positions of each beam in each hop beam slot are determined according to the following rules:

[0090] If 0 ≤ x L If ≤1, then the corresponding wave position is wave position 1;

[0091] If 1 < x L If ≤2, then the corresponding wave position is wave position 2;

[0092] If 2 < x L If ≤3, then the corresponding wave position is wave position 3;

[0093] If 3 < x L If the value is ≤4, then the corresponding wave position is wave position 4;

[0094] If 4 < x L If ≤5, then the corresponding wave position is wave position 5;

[0095] If 5 < x L If ≤6, then the corresponding wave position is wave position 6; and

[0096] If 6 < x L If ≤7, then the corresponding wave position is wave position 7.

[0097] In this way, the abstract element values ​​in the optimal particle vector are transformed into beam position scanning instructions for each satellite beam in specific time slots within the beam hopping period. Ultimately, the low-interference beam position planning scheme represented by the optimal particle vector is implemented as an executable beam operation, providing a clear execution basis for the satellite to accurately complete beam position scanning of each coverage area and effectively suppress co-channel interference.

[0098] Optionally, the operation of determining sub-frequency bands adapted to multiple beams of the satellite includes: acquiring parameter features of the coverage area of ​​the multiple beams, wherein the parameter features are used to characterize the communication link characteristics between the satellite and the coverage area; and inputting the parameter features into a pre-trained neural network model to determine the sub-frequency bands adapted to the multiple beams.

[0099] Specifically, determining the matching sub-bands for multiple satellite beams first requires acquiring the parameter characteristics of the coverage areas of multiple beams. These parameter characteristics are key indicators that directly or indirectly characterize the quality of the communication link between the satellite and the corresponding coverage area. Differences in these key indicators directly affect the sub-band matching effect (for example, high-frequency sub-bands are more sensitive to rain attenuation; if the coverage area has a high rainfall rate, high-frequency sub-bands should be prioritized for exclusion or adjustment). Therefore, acquiring parameter characteristics essentially provides a data foundation for subsequent sub-band matching, preventing sub-band selection from being detached from the actual scenario.

[0100] Based on this, the appropriate sub-band is determined through a pre-trained neural network model. This neural network model, after training, possesses a reliable mapping capability of "input parameter features → output appropriate sub-band". During training, "parameter feature data" of different beam coverage areas in history are used as input samples, while "the sub-band with the best communication quality in actual use in this area" (i.e., the sub-band with the highest signal-to-interference-plus-noise ratio, the most stable data transmission rate, and the lowest service interruption rate) is used as label samples. The model's weight parameters are continuously adjusted through the backpropagation algorithm, enabling the model to gradually learn the correlation between different combinations of parameter features and the optimal sub-band (for example, "high user density + high service traffic" features require matching a large bandwidth sub-band, and "high rainfall rate + low elevation angle" features require matching a sub-band with strong anti-attenuation capability).

[0101] Once the model training is complete and reaches a preset accuracy (e.g., sub-band recommendation accuracy ≥ 90%), the parameter characteristics of the current beam coverage area to be matched are input into the model. The model will then perform inference calculations based on the learned correlation patterns and directly output the most suitable sub-band for that beam. For example, if the parameter characteristics of a beam coverage area are "low average rainfall rate, high user density, and high business traffic demand", the model will infer and output a sub-band with a large bandwidth and low impact from rain attenuation; if the parameter characteristics of another beam coverage area are "high average rainfall rate, low user density, and small beam elevation angle", the model will output a sub-band with strong anti-attenuation capability and low sensitivity to propagation loss.

[0102] By employing the above methods, we can ensure the scientific nature of sub-band selection and achieve precise matching between each beam and sub-band, laying the foundation for subsequent suppression of co-channel interference.

[0103] Optionally, the operation of acquiring parameter characteristics of multiple coverage areas of a satellite includes acquiring the following parameter characteristics: the average rainfall rate of the coverage area; the user density of the coverage area; the service traffic demand of the coverage area; the beam elevation angle of the beam corresponding to the coverage area; and the atmospheric water vapor density of the coverage area. The operation of inputting the parameter characteristics into a pre-trained neural network model and determining the sub-bands adapted to multiple beams through the neural network model includes: inputting the parameter characteristics corresponding to the coverage area into the neural network model and then outputting corresponding classification information through a classifier. The classification information includes the probability values ​​of each sub-band used by the beam corresponding to the coverage area; and selecting the sub-band with the highest probability value as the sub-band used by the beam corresponding to the coverage area.

[0104] Specifically, in order to accurately determine the sub-frequency bands that are compatible with each satellite beam, it is first necessary to obtain the coverage areas S1~S2. m Parameter characteristics F1~F related to satellite communication m The parameter characteristics and physical meanings are as follows:

[0105] 1. Average rainfall rate (unit: mm / h): Its physical significance lies in directly determining the degree of signal attenuation in the Ka band, and the impact of rain attenuation varies significantly among different frequency bands. For example, the 38 GHz band is three times more sensitive to rain attenuation than the 28 GHz band. This parameter can be directly used to determine whether a high-frequency band (such as 38 GHz) is suitable for the coverage area or whether a frequency band with less rain attenuation should be prioritized.

[0106] 2. User density (unit: users / km) 2 This parameter reflects the scale of communication demand in the coverage area. In physical terms, areas with higher user density have a greater demand for communication capacity and need to be matched with larger bandwidth or frequency bands with better anti-interference and stability (such as 28 GHz) to avoid communication congestion caused by concentrated users.

[0107] 3. Business traffic requirements (unit: Mbps / km) 2 ): Directly represents the communication load intensity of the coverage area. Physically, the higher the service traffic demand, the higher the requirement for frequency band bandwidth. At this time, it is necessary to prioritize matching frequency bands with large bandwidth characteristics (such as 38GHz) to meet the high traffic data transmission demand.

[0108] 4. Beam elevation angle (unit: degrees): Its physical meaning is directly related to signal propagation path loss. The lower the elevation angle, the longer the propagation path of the satellite signal to the coverage area, and the more significant the impact of rain attenuation and atmospheric loss on the signal, especially the stronger attenuation effect on high-frequency signals (such as 38 GHz). This parameter characteristic can help determine the applicability of high-frequency bands in this area;

[0109] 5. Atmospheric water vapor density (unit: g / m³) 3 Compared to relative humidity, this parameter can more directly and accurately reflect the degree of absorption of high-frequency (frequency > 30 GHz) signals by the atmospheric environment, and is a key indicator for evaluating the communication performance of high-frequency bands in the coverage area.

[0110] Where the parameter features F1~F m Any parameter feature F in i ,have:

[0111]

[0112] in,

[0113] For coverage area S i The average rainfall rate;

[0114] For coverage area S i User density;

[0115] For coverage area S i The business traffic requirements;

[0116] For coverage area S i Corresponding beam B i The beam elevation angle; and

[0117] Corresponding to coverage area S i Atmospheric water vapor density.

[0118] By obtaining parameter features F1~F that are strongly correlated with communication link performance m It can comprehensively depict the differences in communication environment and needs of each coverage area, providing data support for subsequent precise matching and adaptation of sub-frequency bands through neural network models, and ensuring that the selection of sub-frequency bands is highly consistent with the actual situation of the coverage area.

[0119] Then, the parameter features F1~F corresponding to each coverage area are... m Input the pre-trained neural network model into each beam B1~B1 to determine the relationship with each beam. m Matched sub-bands. (Reference) Figure 8 As shown, with coverage area S i Corresponding parameter feature F i After being input into the neural network, the softmax classifier outputs the corresponding classification information H. i :

[0120]

[0121] in, Represented as beam B i Assign the probability value of the j-th sub-band. Select the sub-band with the highest probability value as the one corresponding to beam B. i The corresponding sub-bands. This allows us to determine the frequency bands for each beam B1~B. m The allocated sub-bands N1~N4 are shown in the table below:

[0122] Table 1

[0123]

[0124] As shown in Table 1, “Y” indicates that the sub-band and the beam are compatible, and “N” indicates that the sub-band and the coverage area are not compatible. It should be noted that this application uniquely assigns the most suitable sub-band to each beam.

[0125] Through the above methods, precise matching of each satellite beam with its sub-bands was achieved, effectively meeting the objective needs of different coverage areas for suitable sub-bands. This provides a more adaptable sub-band foundation for subsequent optimization of beamline scanning strategies and suppression of co-channel interference.

[0126] In addition, refer to Figure 1 As shown, according to a second aspect of this embodiment, a storage medium is provided. The storage medium includes a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0127] Therefore, according to this embodiment, this application, while meeting the objective requirements of the environment of each beam coverage area for adaptable sub-frequency bands, effectively avoids coverage overlap and signal cross-interference of adjacent co-frequency sub-beams by optimizing the beam position scanning scheduling scheme within the beam-hopping period. This solves the technical problem in the prior art where the objective requirements of the environmental differences of each beam coverage area for adaptable sub-frequency bands are not fully considered, leading to adjacent beams still potentially using the same sub-frequency band for scanning in practical applications, thus failing to effectively solve co-frequency interference.

[0128] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0129] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0130] Example 2

[0131] Figure 9 A satellite beam scanning scheduling device for preventing co-channel interference is shown according to this embodiment, which corresponds to the method according to Embodiment 1. (Reference) Figure 9 As shown, the device includes: an adaptation module 910 for determining sub-bands adapted to multiple beams of the satellite respectively; an initialization module 920 for initializing a particle swarm, wherein each element of the particle vector of the particle swarm indicates the wave positions scanned by each beam of the satellite in each hop beam time slot of the hop beam period through different numerical ranges; a construction module 930 for constructing an adaptation function, wherein the function value of the adaptation function is related to the severity of co-channel interference in the satellite coverage area; an iteration module 940 for iteratively optimizing the particle swarm according to the particle swarm optimization algorithm to determine the optimal particle vector; and a determination module 950 for determining the wave positions scanned by each beam in each hop beam time slot of the hop beam period based on the optimal particle vector.

[0132] Optionally, the operation of initializing the particle swarm includes: generating multiple particle vectors; randomly encoding the elements of each particle vector according to pre-set constraints to determine the wave positions corresponding to different elements in each particle vector; and randomly sampling within the corresponding numerical range according to the wave positions corresponding to different elements in each particle vector to determine the values ​​of different elements in each particle vector, wherein the constraints stipulate that multiple beams scan all their corresponding wave positions within the beam-hopping period.

[0133] Optionally, the operation of constructing the fitness function includes constructing a fitness function S(X) as follows:

[0134] (1)

[0135] Where I1(X) represents the time slots of each beam B1~B according to the particle vector X in each beam skipping beam slot. mIn the case of beam allocation, the number of overlapping regions formed by two beams of the same frequency band appearing throughout the entire beam-hopping period; and I2(X) represents the number of beams B1~B1 in each beam-hopping time slot according to the particle vector X. m In the case of assigned beam positions, the number of overlapping regions formed by the overlap of three beams in the same frequency band that occur throughout the entire hopping beam period.

[0136] Optionally, the iterative optimization of the particle swarm according to the particle swarm optimization algorithm includes updating each particle and its corresponding velocity coefficient according to the following formula:

[0137] (2)

[0138] （3）

[0139] Where X k t and V k t Let X and Y represent the particle determined in the t-th iteration, respectively. k and velocity coefficient V k ;X k t+1 and V k t+1 Let X and Y represent the particle determined in the (t+1)th iteration, respectively. k and velocity coefficient V k Xbest k t For particle X k The individual optimal solution determined in the t-th iteration; Xbest t is the global optimal solution determined by the particle swarm in the t-th iteration; w is the inertia factor, which takes a non-negative value; c1 and c2 are learning factors, used to adjust the convergence speed of the algorithm; and r1 and r2 are random numbers between 0 and 1.

[0140] Optionally, the operation of determining the wave position scanned by each beam in each hopping beam slot of the hopping beam period according to the optimal particle vector includes: determining the wave position corresponding to the value of each element in the optimal particle vector; and determining the wave position of each beam in each hopping beam slot according to the beam corresponding to each element in the optimal particle vector and the hopping beam slot.

[0141] Optionally, the operation of determining sub-frequency bands adapted to multiple beams of the satellite includes: acquiring parameter features of the coverage area of ​​the multiple beams, wherein the parameter features are used to characterize the communication link characteristics between the satellite and the coverage area; and inputting the parameter features into a pre-trained neural network model to determine the sub-frequency bands adapted to the multiple beams.

[0142] Optionally, the operation of acquiring parameter characteristics of multiple coverage areas of a satellite includes acquiring the following parameter characteristics: the average rainfall rate of the coverage area; the user density of the coverage area; the service traffic demand of the coverage area; the beam elevation angle of the beam corresponding to the coverage area; and the atmospheric water vapor density of the coverage area. The operation of inputting the parameter characteristics into a pre-trained neural network model and determining the sub-bands adapted to multiple beams through the neural network model includes: inputting the parameter characteristics corresponding to the coverage area into the neural network model and then outputting corresponding classification information through a classifier. The classification information includes the probability values ​​of each sub-band used by the beam corresponding to the coverage area; and selecting the sub-band with the highest probability value as the sub-band used by the beam corresponding to the coverage area.

[0143] Therefore, according to this embodiment, this application, while meeting the objective requirements of the environment of each beam coverage area for adaptable sub-frequency bands, effectively avoids coverage overlap and signal cross-interference of adjacent co-frequency sub-beams by optimizing the beam position scanning scheduling scheme within the beam-hopping period. This solves the technical problem in the prior art where the objective requirements of the environmental differences of each beam coverage area for adaptable sub-frequency bands are not fully considered, leading to adjacent beams still potentially using the same sub-frequency band for scanning in practical applications, thus failing to effectively solve co-frequency interference.

[0144] Example 3

[0145] Figure 10 A satellite beam scanning scheduling device for preventing co-channel interference is shown according to this embodiment, which corresponds to the method described according to Embodiment 1. (Reference) Figure 10 As shown, the device includes: a processor 1010; and a memory 1020 connected to the processor 1010, for providing the processor 1010 with instructions to process the following steps: determining sub-bands adapted to multiple beams of the satellite respectively; initializing a particle swarm, wherein each element of the particle vector of the particle swarm indicates the position scanned by each beam of the satellite in each hop beam time slot of the hop beam period through different numerical ranges; constructing an adaptation function, wherein the function value of the adaptation function is related to the severity of co-channel interference in the satellite coverage area; iteratively optimizing the particle swarm according to a particle swarm optimization algorithm to determine the optimal particle vector; and determining the position scanned by each beam in each hop beam time slot of the hop beam period based on the optimal particle vector.

[0146] Optionally, the operation of initializing the particle swarm includes: generating multiple particle vectors; randomly encoding the elements of each particle vector according to pre-set constraints to determine the wave positions corresponding to different elements in each particle vector; and randomly sampling within the corresponding numerical range according to the wave positions corresponding to different elements in each particle vector to determine the values ​​of different elements in each particle vector, wherein the constraints stipulate that multiple beams scan all their corresponding wave positions within the beam-hopping period.

[0147] Optionally, the operation of constructing the fitness function includes constructing a fitness function S(X) as follows:

[0148] (1)

[0149] Where I1(X) represents the time slots of each beam B1~B according to the particle vector X in each beam skipping beam slot. m In the case of beam allocation, the number of overlapping regions formed by two beams of the same frequency band appearing throughout the entire beam-hopping period; and I2(X) represents the number of beams B1~B1 in each beam-hopping time slot according to the particle vector X. m In the case of assigned beam positions, the number of overlapping regions formed by the overlap of three beams in the same frequency band that occur throughout the entire hopping beam period.

[0150] Optionally, the iterative optimization of the particle swarm according to the particle swarm optimization algorithm includes updating each particle and its corresponding velocity coefficient according to the following formula:

[0151] (2)

[0152] （3）

[0153] Where X k t and V k t Let X and Y represent the particle determined in the t-th iteration, respectively. k and velocity coefficient V k ;X k t+1 and V k t+1 Let X and Y represent the particle determined in the (t+1)th iteration, respectively. k and velocity coefficient V k Xbest k t For particle X k The individual optimal solution determined in the t-th iteration; Xbest tis the global optimal solution determined by the particle swarm in the t-th iteration; w is the inertia factor, which takes a non-negative value; c1 and c2 are learning factors, used to adjust the convergence speed of the algorithm; and r1 and r2 are random numbers between 0 and 1.

[0154] Optionally, the operation of determining the wave position scanned by each beam in each hopping beam slot of the hopping beam period according to the optimal particle vector includes: determining the wave position corresponding to the value of each element in the optimal particle vector; and determining the wave position of each beam in each hopping beam slot according to the beam corresponding to each element in the optimal particle vector and the hopping beam slot.

[0155] Optionally, the operation of determining sub-frequency bands adapted to multiple beams of the satellite includes: acquiring parameter features of the coverage area of ​​the multiple beams, wherein the parameter features are used to characterize the communication link characteristics between the satellite and the coverage area; and inputting the parameter features into a pre-trained neural network model to determine the sub-frequency bands adapted to the multiple beams.

[0156] Optionally, the operation of acquiring parameter characteristics of multiple coverage areas of a satellite includes acquiring the following parameter characteristics: the average rainfall rate of the coverage area; the user density of the coverage area; the service traffic demand of the coverage area; the beam elevation angle of the beam corresponding to the coverage area; and the atmospheric water vapor density of the coverage area. The operation of inputting the parameter characteristics into a pre-trained neural network model and determining the sub-bands adapted to multiple beams through the neural network model includes: inputting the parameter characteristics corresponding to the coverage area into the neural network model and then outputting corresponding classification information through a classifier. The classification information includes the probability values ​​of each sub-band used by the beam corresponding to the coverage area; and selecting the sub-band with the highest probability value as the sub-band used by the beam corresponding to the coverage area.

[0157] Therefore, according to this embodiment, this application, while meeting the objective requirements of the environment of each beam coverage area for adaptable sub-frequency bands, effectively avoids coverage overlap and signal cross-interference of adjacent co-frequency sub-beams by optimizing the beam position scanning scheduling scheme within the beam-hopping period. This solves the technical problem in the prior art where the objective requirements of the environmental differences of each beam coverage area for adaptable sub-frequency bands are not fully considered, leading to adjacent beams still potentially using the same sub-frequency band for scanning in practical applications, thus failing to effectively solve co-frequency interference.

[0158] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0159] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0161] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0163] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0164] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A satellite beam scanning scheduling method to prevent co-channel interference, characterized in that, include: Determine the sub-frequency bands that are adapted to the multiple beams of the satellite; Initialize a particle swarm, wherein the particle swarm is a set of candidate schemes for searching the optimal beam position scanning strategy. Each particle corresponds to a complete beam position scanning strategy. Each element of the particle vector of the particle swarm indicates the beam position scanned by each beam of the satellite in each hop beam slot of the hop beam period through the numerical range to which the element value belongs. The dimension of the particle vector is determined by the number of beams of the satellite and the number of hop beam slots included in a single hop beam period. An adaptive function is constructed, wherein the function value of the adaptive function is related to the severity of co-channel interference within the satellite coverage area. If the number of overlapping areas of the co-channel sub-beams is greater, the interference quantization value output by the adaptive function is larger, indicating that the co-channel interference problem of the sub-band allocation scheme is more serious; conversely, if the number of overlapping areas of the co-channel sub-beams is smaller, the interference quantization value output by the adaptive function is smaller, indicating that the interference control effect of the sub-band allocation scheme is better. The particle swarm is iteratively optimized using a particle swarm optimization algorithm to determine the optimal particle vector; and Based on the optimal particle vector, the wave position scanned by each beam in each hop beam time slot of the hop beam period is determined.

2. The method according to claim 1, characterized in that, The operations for initializing the particle swarm include: Generate multiple particle vectors; Based on pre-set constraints, the elements of each particle vector are randomly encoded to determine the wave positions corresponding to different elements in each particle vector; and Based on the wave position corresponding to different elements in each particle vector, random sampling is performed within the corresponding numerical range to determine the values ​​of different elements in each particle vector. The constraint condition stipulates that the plurality of beams scan all their respective positions within the hopping beam period.

3. The method according to claim 1, characterized in that, The operation of constructing the fitness function includes constructing the fitness function S(X) as described below: ； Where I1(X) represents the time slots of each beam B1~B according to the particle vector X in each beam skipping beam slot. m In the case of beam assignment, the number of overlapping regions caused by two beams of the same frequency band appearing throughout the entire beam-hopping period; and I2(X) represents the particle vector X in each beam skipping time slot for each beam B1~B1. m In the case of assigned beam positions, the number of overlapping regions formed by the overlap of three beams in the same frequency band that occur throughout the entire hopping beam period.

4. The method according to claim 1, characterized in that, According to the particle swarm optimization algorithm, the operation of iterative optimization of the particle swarm includes updating each particle and its corresponding velocity coefficient according to the following formula: ； ； Where X k t and V k t Let X and Y represent the particle determined in the t-th iteration, respectively. k and velocity coefficient V k ;X k t+1 and V k t+1 Let X and Y represent the particle determined in the (t+1)th iteration, respectively. k and velocity coefficient V k Xbest k t For particle X k The individual optimal solution determined in the t-th iteration; Xbest t is the global optimal solution determined by the particle swarm in the t-th iteration; w is the inertia factor, which takes a non-negative value; c1 and c2 are learning factors, used to adjust the convergence speed of the algorithm; and r1 and r2 are random numbers between 0 and 1.

5. The method according to claim 1, characterized in that, The operation of determining the wave positions scanned by each beam in each hop beam time slot of the hop beam period based on the optimal particle vector includes: Determine the wave position corresponding to the value of each element in the optimal particle vector; and Based on the beams corresponding to each element in the optimal particle vector and the hopping beam slots, the wave position of each beam in each hopping beam slot is determined.

6. The method according to claim 1, characterized in that, The operation of determining sub-bands adapted to multiple beams of the satellite includes: Obtain parameter characteristics of the coverage area of ​​the plurality of beams, wherein the parameter characteristics are used to characterize the communication link characteristics between the satellite and the coverage area; and The parameter features are input into a pre-trained neural network model, and the neural network model determines the sub-bands that are adapted to the plurality of beams respectively.

7. The method according to claim 6, characterized in that, The operation of obtaining parameter characteristics of multiple coverage areas of a satellite includes obtaining the following parameter characteristics: The average rainfall rate of the covered area; User density in the coverage area; The service traffic requirements of the coverage area; The beam elevation angle of the beam corresponding to the coverage area; and The atmospheric water vapor density of the covered area, and wherein The operation of inputting the parameter features into a pre-trained neural network model and determining the sub-frequency bands adapted to the multiple beams through the neural network model includes: After the parameter features corresponding to the coverage area are input into the neural network model, the corresponding classification information is output by the classifier, wherein the classification information includes the probability value of the beam corresponding to the coverage area using each sub-frequency band; as well as The sub-band with the highest probability value is selected as the sub-band used by the beam corresponding to the coverage area.

8. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, the method described in any one of claims 1 to 7 is performed by a processor.

9. A satellite beam scanning and scheduling device for preventing co-channel interference, characterized in that, include: An adapter module is used to determine the sub-bands that are adapted to multiple beams of the satellite, respectively; An initialization module is used to initialize a particle swarm, wherein the particle swarm is a set of candidate schemes for searching the optimal beam position scanning strategy. Each particle corresponds to a complete beam position scanning strategy. Each element of the particle vector of the particle swarm indicates the beam position scanned by each beam of the satellite in each hop beam slot of the hop beam period through the numerical range to which the element value belongs. The dimension of the particle vector is determined by the number of beams of the satellite and the number of hop beam slots included in a single hop beam period. A construction module is used to construct an adaptation function, wherein the function value of the adaptation function is related to the severity of co-channel interference within the satellite coverage area. If the number of overlapping areas of the co-channel sub-beams is greater, the interference quantization value output by the adaptation function is larger, indicating that the co-channel interference problem of the sub-band allocation scheme is more serious; conversely, if the number of overlapping areas of the co-channel sub-beams is smaller, the interference quantization value output by the adaptation function is smaller, indicating that the interference control effect of the sub-band allocation scheme is better. An iterative module is used to iteratively optimize the particle swarm according to the particle swarm optimization algorithm to determine the optimal particle vector; and The determination module is used to determine the wave position scanned by each beam in each hop beam time slot of the hop beam period based on the optimal particle vector.

10. A satellite beam scanning and scheduling device for preventing co-channel interference, characterized in that, include: processor; as well as A memory, connected to the processor, for providing the processor with instructions to perform the following processing steps: Determine the sub-frequency bands that are adapted to the multiple beams of the satellite; Initialize a particle swarm, wherein the particle swarm is a set of candidate schemes for searching the optimal beam position scanning strategy. Each particle corresponds to a complete beam position scanning strategy. Each element of the particle vector of the particle swarm indicates the beam position scanned by each beam of the satellite in each hop beam slot of the hop beam period through the numerical range to which the element value belongs. The dimension of the particle vector is determined by the number of beams of the satellite and the number of hop beam slots included in a single hop beam period. An adaptive function is constructed, wherein the function value of the adaptive function is related to the severity of co-channel interference within the satellite coverage area. If the number of overlapping areas of the co-channel sub-beams is greater, the interference quantization value output by the adaptive function is larger, indicating that the co-channel interference problem of the sub-band allocation scheme is more serious; conversely, if the number of overlapping areas of the co-channel sub-beams is smaller, the interference quantization value output by the adaptive function is smaller, indicating that the interference control effect of the sub-band allocation scheme is better. The particle swarm is iteratively optimized using a particle swarm optimization algorithm to determine the optimal particle vector; and the wave position scanned by each beam in each hop beam slot of the hop beam period is determined based on the optimal particle vector.

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