Beam switching prediction method, device and storage medium for antenna array

Abstract

The present application discloses a beam switching prediction method, device and storage medium for an antenna array, including: determining the first position of the mobile body in the next period based on the navigation information of the predetermined mobile body, wherein the mobile body is provided with an antenna array; determining multiple beams connected to the mobile body at the first position of the mobile body in the next period according to the ephemeris information and the beam scheduling information of each first satellite; predicting the target beam connected to the mobile body from multiple beams based on a preset neural network model; generating a corresponding antenna connection optimization function based on an initialized population including multiple first antenna connection vectors corresponding to the antenna array, and determining the optimal solution of the target optimization function corresponding to the multiple antenna connection optimization functions using a genetic algorithm; and predicting the target antenna connected to the target beam when the optimal solution of the target optimization function is determined. The technical effect of reducing the switching overhead of the antenna array is achieved.

Description

Technical Field

[0001] The present application relates to the field of satellite communication technology, and in particular to a beam switching prediction method, device and storage medium for an antenna array. Background Art

[0002] As described in the prior art, since both satellites and trains have high mobility, during the movement of the train and the satellite, the train may cross different beam areas. That is, handover may occur. It is worth noting that the above-mentioned high mobility may not only be a train, but also a ship or a car.

[0003] Beam switching can be divided into intra-satellite switching, inter-satellite switching and ground switching. Intra-satellite switching refers to the switching of mobile objects (including trains, ships and cars) through different beams between the same satellite. The switching behavior is managed by the same satellite, so the required switching overhead is relatively small. Inter-satellite switching refers to the beam switching caused by the high-speed movement of the satellite or the high-speed movement of the mobile object, which causes the train to cross the beam coverage of two adjacent satellites.

[0004] Unlike traditional beam switching, the running trajectories of the mobile body and satellite are determined, and beam switching is predictable. Therefore, the neighbor list when the switching is triggered can be calculated based on the satellite ephemeris information and the mobile body running time information. However, the premise of being able to predict the switched beam is that the beam to be switched by the mobile body in the next time period can only be determined when there is only one connectable satellite or the target satellite among multiple satellites has been determined. When the mobile body needs to perform inter-satellite switching, and there are multiple satellites that can communicate with the mobile body in the next time period, the mobile body cannot predict the beam to be switched in the next time period because it has not yet determined the target beam among multiple beams.

[0005] In addition, since the mobile body is often equipped with multiple antennas with different transmission angles (i.e., antenna arrays), even if the beam to be switched by the mobile body in the next period has been determined, it is necessary to further determine the antenna in the antenna array that is connected to the switched beam. Therefore, in the above two switching processes (i.e., the satellite switching process and the antenna switching process), a certain switching overhead will be generated, and the greater the switching overhead, the greater the additional time, space and resource costs required.

[0006] The publication number is CN116209021A, and the name is beam switching method and device based on channel prediction. The method includes obtaining state information in the vehicle networking scene in each time slot; inputting the state information into the reinforcement learning Q value network to obtain the Q value corresponding to all actions, and using the ε greedy method to output the corresponding action in the current Q value; executing the action to obtain the corresponding reward value and the state information of the next time slot; storing the state information, action, reward value of the current time slot and the state information of the next time slot in the experience pool; sampling data from the experience pool, and training the reinforcement learning Q value network.

[0007] The publication number is CN115833889A, and the name is a beam switching method and corresponding device based on diamond search in high-speed mobile scenarios. The method includes: taking all the fine beams in the target coarse beam where the current communication fine beam of the receiving end is located, and all the fine beams in at least two coarse beams adjacent to the target coarse beam as the beam range of the diamond search, using the diamond search method to determine the first target fine beam corresponding to the first radio frequency link, and using the first target fine beam to switch the fine beam used by the first radio frequency link, and at the same time, based on the first target fine beam, determining the fine beams used by other radio frequency links.

[0008] In view of the technical problem that the above-mentioned existing technology exists in that a certain switching overhead will be generated in the satellite switching process and the antenna switching process, and the greater the switching overhead, the greater the time, space and resource costs required, and the switching overhead of the above process cannot be reduced, no effective solution has been proposed yet. Summary of the invention

[0009] The embodiments of the present disclosure provide a beam switching prediction method, device and storage medium for an antenna array, so as to at least solve the technical problem in the prior art that a certain switching overhead will be generated in both the satellite switching process and the antenna switching process, and the greater the switching overhead, the greater the time, space and resource cost required, and the switching overhead of the above process cannot be reduced at present.

[0010] According to one aspect of an embodiment of the present disclosure, a beam switching prediction method for an antenna array is provided, comprising: determining a first position of a moving body in a next time period based on predetermined navigation information of a moving body, wherein the moving body is provided with an antenna array; determining a plurality of beams connected to the moving body at the first position of the moving body in the next time period based on ephemeris information and beam scheduling information of each first satellite; predicting a target beam connected to the moving body from a plurality of beams based on a preset neural network model; generating a corresponding antenna connection optimization function based on an initialized population including a plurality of first antenna connection vectors corresponding to the antenna array, and determining an optimal solution of a target optimization function corresponding to the plurality of antenna connection optimization functions using a genetic algorithm, wherein the antenna connection optimization function is used to indicate a degree of fitness of the connection between each antenna in the antenna array and the target beam, and the target optimization function is used to indicate a maximum degree of fitness among the plurality of antenna connection optimization functions; and predicting a target antenna connected to the target beam when the optimal solution of the target optimization function is determined.

[0011] According to another aspect of an embodiment of the present disclosure, a storage medium is further provided, the storage medium including a stored program, wherein when the program is running, a processor executes any one of the methods described above.

[0012] According to another aspect of an embodiment of the present disclosure, a beam switching prediction device for an antenna array is also provided, comprising: a position determination module, for determining a first position of a mobile body in a next time period based on predetermined navigation information of the mobile body, wherein the mobile body is provided with an antenna array; a first beam determination module, for determining a plurality of beams connected to the mobile body at the first position of the mobile body in the next time period according to ephemeris information and beam scheduling information of each first satellite; a beam prediction module, for predicting a target beam connected to the mobile body from a plurality of beams based on a preset neural network model; an optimal solution determination module, for generating a corresponding antenna connection optimization function based on an initialized population including a plurality of first antenna connection vectors corresponding to the antenna array, and determining an optimal solution of a target optimization function corresponding to the plurality of antenna connection optimization functions using a genetic algorithm, wherein the antenna connection optimization function is used to indicate a degree of fitness of the connection between each antenna in the antenna array and the target beam, and the target optimization function is used to indicate a maximum degree of fitness among the plurality of antenna connection optimization functions; and a target antenna prediction module, for predicting a target antenna connected to the target beam when the optimal solution of the target optimization function is determined.

[0013] According to another aspect of an embodiment of the present disclosure, a beam switching prediction device for an antenna array is also provided, comprising: a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: determining a first position of a mobile body in a next time period based on predetermined navigation information of the mobile body, wherein the mobile body is provided with an antenna array; determining multiple beams connected to the mobile body at the first position of the mobile body in the next time period based on ephemeris information and beam scheduling information of each first satellite; predicting a target beam connected to the mobile body from multiple beams based on a preset neural network model; generating a corresponding antenna connection optimization function based on an initialized population including multiple first antenna connection vectors corresponding to the antenna array, and determining an optimal solution of a target optimization function corresponding to the multiple antenna connection optimization functions using a genetic algorithm, wherein the antenna connection optimization function is used to indicate a degree of fitness of each antenna in the antenna array connected to a target beam, and the target optimization function is used to indicate a maximum degree of fitness among multiple antenna connection optimization functions; and predicting a target antenna connected to the target beam when the optimal solution of the target optimization function is determined.

[0014] The present application discloses a beam switching prediction method for an antenna array. First, the processor determines the first position of the mobile body in the next time period based on the navigation information of the predetermined mobile body. Then, the processor determines multiple beams connected to the mobile body at the first position of the mobile body in the next time period according to the ephemeris information and the beam scheduling information of each first satellite. Furthermore, the processor predicts the target beam connected to the mobile body from multiple beams based on a preset neural network model. Thereafter, the processor generates a corresponding antenna connection optimization function based on an initialized population including multiple first antenna connection vectors corresponding to the antenna array, and uses a genetic algorithm to determine the optimal solution of the target optimization function corresponding to the antenna connection optimization function. Finally, the processor predicts the target antenna connected to the target beam when determining the optimal solution of the target optimization function.

[0015] With reference to the above-mentioned contents, it can be known that since the present application first predicts the target beam to be connected to the mobile body from multiple beams based on a preset neural network model, and then determines the optimal solution of the target optimization function corresponding to the multiple antenna connection optimization functions based on the antenna connection optimization function and using a genetic algorithm, it is possible to finally predict the target antenna connected to the target beam. Therefore, whether in the process of beam switching (i.e., predicting the target beam to be connected to the mobile body from multiple beams) or in the process of antenna switching (i.e., predicting the target antenna to be connected to the target beam from the antenna array), the required switching overhead is minimized. This achieves the technical effect of reducing the time, space and resource costs consumed when the antenna array of the mobile body switches the connection beam.

[0016] This solves the technical problem in the prior art that a certain switching overhead will be generated in the satellite switching process and the antenna switching process, and the greater the switching overhead, the greater the time, space and resource costs required, and the switching overhead of the above process cannot be reduced at present. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0018] Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present application;

[0019] Figure 2A is a schematic diagram of the hardware architecture of the satellite according to Embodiment 1 of the present application;

[0020] Figure 2B is a schematic diagram of the hardware architecture of the mobile object according to Embodiment 1 of the present application;

[0021] Figure 3 is a flowchart of a beam switching prediction method for an antenna array according to Embodiment 1 of the present application;

[0022] Figure 4 is a schematic diagram of a device for a beam switching prediction method for an antenna array according to Embodiment 2 of the present application;

[0023] Figure 5 It is a schematic diagram of a device for a beam switching prediction method for an antenna array according to Example 3 of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0026] Example 1

[0027] According to this embodiment, a method embodiment for beam switching prediction of an antenna array is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] Figure 1 is a schematic diagram of beam switching performed by a mobile object according to an embodiment of the present application. Figure 1 As shown, the system includes: a plurality of satellites 101-10n and a mobile body 20 provided with an antenna array. The antenna array of the mobile body 20 includes a plurality of antennas, and different antennas cover different angles. Further, Figure 1 The mobile body 20 shown is within the beam coverage of the satellite 101, and is connected to the satellite 101 through the antenna array and the corresponding beam. It is worth noting that the mobile body 20 will enter the beam coverage of the satellites 102 to 10n in the next period, so that the mobile body 20 can also communicate with each satellite 102 to 10n through the antenna array and the beams emitted by each satellite 102 to 10n.

[0029] Figure 2A It further shows Figure 1 Schematic diagram of the hardware architecture of satellites 101~10n. Figure 2AAs shown, satellites 101~10n include an integrated electronic system, which includes: a processor, a memory, a bus management module and a communication interface. The memory is connected to the processor, so that the processor can access the memory, read the 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 is also connected to a bus such as a CAN bus. Therefore, the processor can communicate with the satellite-borne peripherals connected to the bus through the bus managed by the bus management module. In addition, the processor is also connected to communication devices such as cameras, star sensors, measurement and control transponders, and data transmission equipment via the communication interface. It can be understood by those skilled in the art that Figure 2A The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 2A More or fewer components as shown, or with Figure 2A Different configurations are shown.

[0030] Figure 2B It further shows Figure 1 Schematic diagram of the hardware architecture of the mobile body 20. Figure 2B As shown, the mobile body 20 may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, the transmission device, and the input / output interface are connected to the processor via a bus. In addition, it may also include: a display, a keyboard, and a cursor control device connected to the input / output interface. A person skilled in the art can understand that Figure 2B The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 2B More or fewer components as shown, or with Figure 2B Different configurations are shown.

[0031] It should be noted that Figure 2A and Figure 2B The one or more processors and / or other data processing circuits shown in the figure may generally be referred to as "data processing circuitry" herein. The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other elements in the computing device. As involved in the embodiments of the present disclosure, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0032] Figure 2A and Figure 2BThe memory shown in the figure can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the beam switching prediction method for the antenna array in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the beam switching prediction method for the antenna array of the above-mentioned application program is realized. The memory may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0033] It should be noted that, in some optional embodiments, the above Figure 2A and Figure 2B The devices shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. Figure 2A and Figure 2B This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the above described apparatus.

[0034] In the above operating environment, according to the first aspect of this embodiment, a beam switching prediction method for an antenna array is provided. The method comprises: Figure 2B The processor implementation in the mobile body 20 shown in FIG. Figure 3 A schematic diagram showing the process of the method is shown in FIG. Figure 3 As shown, the method includes:

[0035] S302: Determine a first position of the moving body in a next time period based on predetermined navigation information of the moving body, wherein the moving body is provided with an antenna array;

[0036] S304: determining, according to the ephemeris information and the beam scheduling information of each first satellite, a plurality of beams connected to the mobile body at the first position of the mobile body in the next time period;

[0037] S306: predicting a target beam for establishing connection with the mobile object from the multiple beams based on a preset neural network model;

[0038] S308: generating a corresponding antenna connection optimization function based on an initialized population including a plurality of first antenna connection vectors corresponding to the antenna array, and determining an optimal solution of a target optimization function corresponding to the plurality of antenna connection optimization functions by using a genetic algorithm, wherein the antenna connection optimization function is used to indicate a degree of fitness of each antenna in the antenna array and the target beam connection, and the target optimization function is used to indicate a maximum degree of fitness among the plurality of antenna connection optimization functions; and

[0039] S310: When an optimal solution of the target optimization function is determined, predict a target antenna connected to the target beam.

[0040] Specifically, first, the processor determines the navigation information of the mobile body 20, and determines the driving route of the mobile body 20 based on the navigation information of the mobile body 20. Thus, when the driving route of the mobile body 20 is determined, the position of the mobile body 20 in the next period (i.e., the first position) can be further determined (S302). For example, the position of the mobile body 20 in the current period is d 1 When the driving route of the moving body 20 is determined, the position of the moving body in the next period can be further determined as d 2 .

[0041] In addition, the mobile body 20 is provided with an antenna array, which includes a plurality of antennas, and the processor stores parameter information related to the antenna array, such as the number of antennas in the antenna array and the installation angle of each antenna, etc. The installation angles of each antenna are different, so that each antenna can communicate with beams at different angles.

[0042] Furthermore, when determining the time information of the next time period, the processor may determine the second position of each first satellite in the next time period according to the pre-stored ephemeris information. The second position is d 1 , the first satellite The second position is d 2 , the first satellite The second position is d 3 , ....., the first satellite The second position is d 4 . Further, when the processor determines the first position of the mobile body 20 in the next period, it can determine a plurality of second satellites corresponding to the first position of the mobile body 20 in the next period. That is, in the next period, the beam coverage can cover the plurality of second satellites in the first position. For example, the first position of the mobile body 20 in the next period is d 2 . Thus, the processor can determine that the next period is also in the first position d 2 A plurality of second satellites or beams covering the first location d 2 That is, the processor can determine the plurality of second satellites corresponding to the mobile object 20. .

[0043] The processor then determines the beams corresponding to the second satellites in the next time period based on the beam scheduling information of the second satellites (S304). The beam scheduling information may be, for example, the beams corresponding to the second satellites in each time period. Which beam to transmit, and the coverage and direction of the beam. Thus, the processor determines the second satellite In the case of beam hopping scheduling information, it can be determined that each second satellite in the next period The transmitted beam .

[0044] Further, the processor predicts the target beam for establishing connection with the mobile body 20 from the beams transmitted by each second satellite based on the preset neural network model (S306). Transmitted beam In the case of Corresponding beam quality information The beam quality information may include, for example, beam communication loss, signal-to-noise ratio, and signal attenuation. Then, the processor calculates the beam quality information based on the information related to each beam. Corresponding beam quality information , determine the The corresponding beam quality vector . Further, the processor will be associated with each beam The corresponding beam quality vector Input to the pre-set neural network model and softmax classifier to output the The corresponding probability value. The larger the probability value, the higher the quality of the beam, and the smaller the overhead required for beam switching; the smaller the probability value, the lower the quality of the beam, and the greater the overhead required for beam switching. Corresponding probability value, determine the target beam among multiple beams .

[0045] Further, when the processor determines the target beam, a corresponding antenna connection optimization function is generated based on the initialization population, and an optimal solution of the target optimization function corresponding to the plurality of antenna connection optimization functions is determined using a genetic algorithm (S308). The initialization population includes a plurality of first antenna connection vectors corresponding to each antenna. Specifically, the processor randomly generates multiple first antenna connection vectors based on multiple antennas. Among them, multiple first antenna connection vectors is a randomly generated initialization satellite connection vector. And wherein the first antenna connection vector , the first antenna connection vector , ..., the first antenna connection vector In addition, it is worth noting that since an antenna can only be connected to one beam, for each antenna, there are only two situations: connection or non-connection. For example, the first antenna connection vector ,exist In the case of , it means that the antenna W1 among multiple antennas is aligned with the target beam Establish a connection, the other antennas W2~W in the multiple antennas v Neither is aligned with the target beam Establish a connection.

[0046] For another example, the first antenna connection vector ,exist and In the case of , it means that the antenna W1 among multiple antennas is aligned with the target beam Establish a connection, antenna W2 among multiple antennas is connected to the target beam Establish a connection, other antennas W3~W among the multiple antennas v Neither is aligned with the target beam Establish a connection.

[0047] And so on.

[0048] For another example, the first antenna connection vector ,exist In the case of , it means any one of the multiple antennas W1~W v Both with target beam Establish a connection.

[0049] After that, when the processor determines the initialization population, it generates the corresponding antenna connection optimization function .in, i =1~u. And among them, Represents the connection vector with the first antenna The corresponding antenna connection optimization function, Represents the connection vector with the first antenna The corresponding antenna connection optimization function, ..., Represents the connection vector with the first antenna The corresponding antenna connection function. It is worth noting that the antenna connection optimization function is the fitness function.

[0050] Further, the processor uses a genetic algorithm to determine the optimization function connected to the multiple antennas The corresponding objective optimization function F. Among them, . And wherein, the target optimization function is used to indicate each antenna W1~W v With target beam The maximum fitness of the connection.

[0051] Finally, the processor determines the optimal solution corresponding to the target optimization function F using a genetic algorithm. That is, the processor determines the optimal solution corresponding to the target beam The most suitable antenna (i.e., the target antenna). For example, if multiple antennas are connected in the optimization function, The adaptability is the largest, which means that the antenna connection optimization function The corresponding first antenna connection vector The adaptability of the antenna W1 to the target beam is the largest. Establishing a connection, other antennas in multiple antennas Neither is aligned with the target beam When a connection is established, the switching overhead required for the next period is minimal.

[0052] For another example, if in the multiple antenna connection optimization function, The adaptability is the largest, which means that the antenna connection optimization function The corresponding first antenna connection vector The adaptability of the antenna to the target beam is the largest. That is, any antenna W1~W v Both with target beam When a connection is established, the switching overhead required in the next period is minimal.

[0053] Referring to the above contents, it can be seen that in this embodiment, since the antenna array includes a plurality of antennas W1 to W v , and each antenna can only communicate with one beam, so multiple antennas W1~W v With target beam There are multiple possible connection modes between them. Furthermore, in the process of switching antennas, different antennas W1~W v With target beam The overhead of the switching process is different, so for the mobile body 20, selecting the antenna with the highest adaptability to communicate with the target beam helps reduce the switching overhead.

[0054] Thus, when the processor uses the genetic algorithm to determine the optimal solution of the target optimization function corresponding to the plurality of antenna connection optimization functions (i.e., when determining the antenna connection optimization function with the greatest degree of fitness), it is possible to determine the antenna (i.e., the target antenna) that is most suitable for communication connection for the target beam. The above content will be described in detail later, so it will not be repeated here.

[0055] Finally, the processor predicts the target antenna connected to the target beam when determining the optimal solution of the target optimization function (S310). The adaptability is the largest, which means that the antenna connection optimization function The corresponding first antenna connection vector The adaptability of each antenna connected to the target beam is the largest. Therefore, when the processor determines the optimal solution of the target optimization function, the target antenna connected to the target beam can be predicted.

[0056] As described in the background technology, unlike traditional beam switching, the running trajectories of the above-mentioned mobile body and satellite are determined, and beam switching is predictable. Therefore, the neighbor list when the switching is triggered can be calculated based on the satellite ephemeris information and the mobile body running time information. However, the premise of being able to predict the switched beam is that the beam to be switched by the mobile body in the next time period can be determined when there is only one connectable satellite or the target satellite among multiple satellites has been determined. When the mobile body needs to perform inter-satellite switching, and there are multiple satellites that can communicate with the mobile body in the next time period, the mobile body cannot predict the beam to be switched in the next time period because the mobile body has not yet determined the target beam among the multiple beams.

[0057] In addition, since the mobile body is often equipped with multiple antennas with different transmission angles (i.e., antenna arrays), even if the beam to be switched by the mobile body in the next period has been determined, it is necessary to further determine the antenna in the antenna array that is connected to the switched beam. Therefore, in the above two switching processes (i.e., the satellite switching process and the antenna switching process), a certain switching overhead will be generated, and the greater the switching overhead, the greater the additional time, space and resource costs required.

[0058] In view of this, the present application provides a beam switching prediction method for an antenna array. And because the present application first predicts the target beam to be connected to a mobile body from multiple beams based on a preset neural network model, and then determines the optimal solution of the target optimization function corresponding to the multiple antenna connection optimization functions based on the antenna connection optimization function and using a genetic algorithm, it is possible to finally predict the target antenna connected to the target beam. Therefore, whether in the process of beam switching (i.e., predicting the target beam to be connected to a mobile body from multiple beams) or in the process of antenna switching (i.e., predicting the target antenna to be connected to a target beam from an antenna array), the required switching overhead is minimized. Thereby achieving the technical effect of reducing the time, space and resource costs consumed when the antenna array of a mobile body switches the connection beam.

[0059] This solves the technical problem in the prior art that a certain switching overhead will be generated in the satellite switching process and the antenna switching process, and the greater the switching overhead, the greater the time, space and resource costs required, and the switching overhead of the above process cannot be reduced at present.

[0060] Optionally, the operation of determining multiple beams for establishing a connection with the mobile body at the first position of the mobile body in the next time period according to the ephemeris information and the beam scheduling information of each first satellite includes: determining the second position of each first satellite in the next time period according to the ephemeris information, and determining multiple second satellites corresponding to the mobile body based on the first position of the mobile body in the next time period; and determining the beams corresponding to each second satellite in the next time period based on the beam scheduling information of each second satellite, wherein the beams corresponding to each second satellite are used to indicate multiple beams for establishing a connection with the mobile body.

[0061] Specifically, when determining the time information of the next time period, the processor can determine the second position of each first satellite in the next time period according to the pre-stored ephemeris information. The second position is d 1 , the first satellite The second position is d 2 , the first satellite The second position is d 3 , ....., the first satellite The second position is d 4. Further, when the processor determines the first position of the mobile body 20 in the next period, it can determine a plurality of second satellites corresponding to the first position of the mobile body 20 in the next period. That is, in the next period, the beam coverage can cover the plurality of second satellites in the first position. For example, the first position of the mobile body 20 in the next period is d 2 . Thus, the processor can determine that the next period is also in the first position d 2 A plurality of second satellites or beams covering the first location d 2 That is, the processor can determine the plurality of second satellites corresponding to the mobile object 20. .

[0062] The processor then calculates the number of satellites based on each second satellite. The beam scheduling information determines the next time period with each second satellite The beam scheduling information may be, for example, the number of beams of each second satellite in each time period. Which beam to transmit, and the coverage and direction of the beam. Thus, the processor determines the second satellite In the case of beam hopping scheduling information, it can be determined that each second satellite in the next period The transmitted beam .

[0063] The above operations thus achieve the technical effect of providing the necessary basis for the subsequent use of a preset neural network model to determine the target beam among multiple beams and ensure that the beam switching overhead is minimized.

[0064] Optionally, based on an initialized population including multiple first antenna connection vectors corresponding to the antenna array, an operation of generating a corresponding antenna connection optimization function includes: determining multiple influencing factors corresponding to each first antenna connection vector in the initialized population, wherein the influencing factors include beam coverage time, number of remaining channels and signal quality; determining a weight ratio corresponding to each influencing factor; and determining multiple antenna connection optimization functions corresponding to each first antenna connection vector based on the multiple influencing factors and the weight ratio corresponding to each influencing factor.

[0065] Specifically, first, when the processor determines multiple antenna connection optimization functions, the processor can further determine multiple influencing factors corresponding to each first antenna connection vector in the initialization population. Among them, there are many factors that affect the antenna switching overhead. In this embodiment, the beam coverage time, the number of remaining channels and the signal quality are selected as factors affecting the antenna switching overhead. Among them, the greater the beam coverage time, the lower the frequency of antenna switching. The more remaining channels, the more balanced the load. The higher the signal quality, the better the signal strength and stability, which can further avoid unnecessary antenna switching due to signal instability.

[0066] Further, the processor determines the weight ratio corresponding to each influencing factor. The weight ratio corresponding to the beam coverage time is set to w 1 , set the weight ratio corresponding to the number of remaining channels to w 2 , and set the weight ratio corresponding to the signal quality to w 3 It is worth noting that the weight ratios corresponding to the above-mentioned influencing factors can be preset according to the actual situation, or determined according to the importance of each influencing factor. The more important the influencing factor is, the higher the weight ratio is; the less important the influencing factor is, the lower the weight ratio is.

[0067] Finally, the processor calculates the weight ratio of each influencing factor according to the multiple influencing factors. w 1 ~ w 3 , determine multiple antenna connection optimization functions corresponding to each first antenna connection vector .in, i =1~u. And .

[0068] Thus, by generating multiple antenna connection optimization functions corresponding to each first antenna connection vector based on multiple influencing factors and the weight ratio corresponding to each influencing factor, a technical effect of reducing the overhead required for switching antennas is achieved.

[0069] Optionally, the operation of using a genetic algorithm to determine the optimal solution of a target optimization function corresponding to multiple antenna connection optimization functions includes: determining multiple second antenna connection vectors based on multiple first antenna connection vectors, wherein the second antenna connection vectors represent sub-generation connection vectors of the multiple first antenna connection vectors; cross-copying each second antenna connection vector and generating multiple third antenna connection vectors; performing mutation operations on each third antenna connection vector and generating multiple fourth satellite connection vectors; and repeating iterations until a termination condition is met, and outputting the optimal solution corresponding to the target optimization function, wherein the termination condition is used to indicate a preset number of iterations.

[0070] Specifically, referring to the above contents, it can be known that the processor randomly generates an initialization population including a plurality of first antenna connection vectors based on a plurality of antennas in the antenna array, wherein the plurality of first antenna connection vectors are "chromosomes".

[0071] Then, the processor selects one or more most representative first antenna connection vectors based on the multiple first antenna connection vectors, and determines multiple second antenna connection vectors based on the selected multiple first antenna connection vectors, wherein the multiple second antenna connection vectors represent sub-generation connection vectors of the multiple first antenna connection vectors.

[0072] Furthermore, the processor cross-copies each second antenna connection vector and generates a plurality of third antenna connection vectors. The processor cross-copies each second antenna connection vector, which helps to explore different areas of the solution space.

[0073] Then, the processor performs a mutation operation on each third antenna connection vector and generates a corresponding fourth antenna connection vector. The processor performs a mutation operation on each fourth antenna connection vector to help increase diversity and prevent the algorithm from converging to a local optimal solution too early.

[0074] Therefore, after the processor cross-copies and mutates each fourth antenna connection vector, multiple fourth connection vectors (i.e., a new generation population) can be generated. And because the multiple second antenna connection vectors (i.e., a sub-generation population) are generated by selecting the most representative (i.e., the most fit) one or more first antenna connection vectors, the fitness of the multiple fourth antenna connection vectors finally generated is also high.

[0075] Finally, the above steps are repeated until the termination condition is met. The termination condition may be, for example, reaching a preset number of iterations, or the average fitness no longer significantly improving. When the termination condition is met, the processor outputs the optimal solution corresponding to the target optimization function. The optimal solution corresponding to the target optimization function is used to indicate that the fitness of each antenna in the antenna array connected to the target beam is the largest.

[0076] Thus, by performing cross-copying, mutation, and repeated iterations on the initialized population, the optimal solution corresponding to the target optimization function can be finally output, thereby achieving the technical effect of being able to determine the antenna corresponding to the minimum switching overhead.

[0077] In addition, it is worth noting that, since the embodiment of the present application uses a pre-set neural network model to determine the target beam among multiple beams with the smallest switching overhead, and also uses a genetic algorithm to determine the target antenna among multiple antennas with the smallest switching overhead, in the embodiment of the present application, the switching overhead required when selecting the switched beam is minimized, and when the switching overhead required when selecting the switched antenna is minimized, the switching overhead required in the process of switching the target antenna of the mobile body to the target beam is minimized. That is, the switching overhead required for the entire switching process is minimized.

[0078] Therefore, according to the first aspect of this embodiment, the technical effect of reducing the time, space and resource costs consumed when the antenna array of the mobile object switches the connection beam is achieved.

[0079] In addition, reference Figure 1 As shown, according to the second aspect of this embodiment, a storage medium is provided, wherein the storage medium includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0080] Therefore, according to this embodiment, the technical effect of reducing the time, space and resource costs consumed when the antenna array of the mobile object switches the connection beam is achieved.

[0081] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0082] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0083] Example 2

[0084] Figure 4 FIG. 4 shows a beam switching prediction device 400 for an antenna array according to this embodiment, and the device 400 corresponds to the method according to embodiment 1. Figure 4 As shown, the device 400 includes: a position determination module 410, which is used to determine the first position of the mobile body in the next time period based on the navigation information of the mobile body determined in advance, wherein the mobile body is provided with an antenna array; a first beam determination module 420, which is used to determine multiple beams connected to the mobile body at the first position of the mobile body in the next time period according to the ephemeris information and the beam scheduling information of each first satellite; a beam prediction module 430, which is used to predict the target beam connected to the mobile body from multiple beams based on a preset neural network model; an optimal solution determination module 440, which is used to generate a corresponding antenna connection optimization function based on an initialized population including multiple first antenna connection vectors corresponding to the antenna array, and determine the optimal solution of the target optimization function corresponding to the multiple antenna connection optimization functions by using a genetic algorithm, wherein the antenna connection optimization function is used to indicate the fitness of each antenna in the antenna array and the target beam, and the target optimization function is used to indicate the maximum fitness among the multiple antenna connection optimization functions; and a target antenna prediction module 450, which is used to predict the target antenna connected to the target beam when the optimal solution of the target optimization function is determined.

[0085] Optionally, the first beam determination module 420 includes: a second satellite determination module, used to determine the second position of each first satellite in the next time period according to the ephemeris information, and determine multiple second satellites corresponding to the mobile body based on the first position of the mobile body in the next time period; and a second beam determination module, used to determine the beams corresponding to each second satellite in the next time period based on the beam scheduling information of each second satellite, wherein the beams corresponding to each second satellite are used to indicate multiple beams for establishing a connection with the mobile body.

[0086] Optionally, the optimal solution determination module 440 includes: an influencing factor determination module, used to determine multiple influencing factors corresponding to each first antenna connection vector in the initialized population, wherein the influencing factors include beam coverage time, the number of remaining channels and signal quality; a weight ratio determination module, used to determine the weight ratio corresponding to each influencing factor; and an optimization function determination module, used to determine multiple antenna connection optimization functions corresponding to each first antenna connection vector based on multiple influencing factors and the weight ratio corresponding to each influencing factor.

[0087] Optionally, the optimal solution determination module 440 includes: a first antenna connection vector determination module, used to determine multiple second antenna connection vectors based on multiple first antenna connection vectors, wherein the second antenna connection vector represents a sub-generation connection vector of the multiple first antenna connection vectors; a second antenna connection vector determination module, used to cross-copy each second antenna connection vector and generate multiple third antenna connection vectors; a third antenna connection vector determination module, used to perform mutation operations on each third antenna connection vector and generate multiple fourth satellite connection vectors; and a repeated iteration module, used to repeat the iteration until a termination condition is met, and output the optimal solution corresponding to the target optimization function, wherein the termination condition is used to indicate a pre-set number of iterations.

[0088] Therefore, according to this embodiment, the technical effect of reducing the time, space and resource costs consumed when the antenna array of the mobile object switches the connection beam is achieved.

[0089] Example 3

[0090] Figure 5 FIG. 5 shows a beam switching prediction device 500 for an antenna array according to this embodiment, and the device 500 corresponds to the method according to embodiment 1. Figure 5As shown, the device 500 includes: a processor 510; and a memory 520, which is connected to the processor 510 and is used to provide the processor 510 with instructions for processing the following processing steps: based on the navigation information of the mobile body determined in advance, determining the first position of the mobile body in the next time period, wherein the mobile body is provided with an antenna array; determining multiple beams connected to the mobile body at the first position of the mobile body in the next time period according to the ephemeris information and the beam scheduling information of each first satellite; predicting the target beam connected to the mobile body from the multiple beams based on the preset neural network model; generating the corresponding antenna connection optimization function based on the initialization population including multiple first antenna connection vectors corresponding to the antenna array, and determining the optimal solution of the target optimization function corresponding to the multiple antenna connection optimization functions by using a genetic algorithm, wherein the antenna connection optimization function is used to indicate the fitness of each antenna in the antenna array connected to the target beam, and the target optimization function is used to indicate the maximum fitness among the multiple antenna connection optimization functions; and predicting the target antenna connected to the target beam when the optimal solution of the target optimization function is determined.

[0091] Optionally, the operation of determining multiple beams for establishing a connection with the mobile body at the first position of the mobile body in the next time period according to the ephemeris information and the beam scheduling information of each first satellite includes: determining the second position of each first satellite in the next time period according to the ephemeris information, and determining multiple second satellites corresponding to the mobile body based on the first position of the mobile body in the next time period; and determining the beams corresponding to each second satellite in the next time period based on the beam scheduling information of each second satellite, wherein the beams corresponding to each second satellite are used to indicate multiple beams for establishing a connection with the mobile body.

[0092] Optionally, based on an initialized population including multiple first antenna connection vectors corresponding to the antenna array, an operation of generating a corresponding antenna connection optimization function includes: determining multiple influencing factors corresponding to each first antenna connection vector in the initialized population, wherein the influencing factors include beam coverage time, number of remaining channels and signal quality; determining a weight ratio corresponding to each influencing factor; and determining multiple antenna connection optimization functions corresponding to each first antenna connection vector based on the multiple influencing factors and the weight ratio corresponding to each influencing factor.

[0093] Optionally, the operation of using a genetic algorithm to determine the optimal solution of a target optimization function corresponding to multiple antenna connection optimization functions includes: determining multiple second antenna connection vectors based on multiple first antenna connection vectors, wherein the second antenna connection vectors represent sub-generation connection vectors of the multiple first antenna connection vectors; cross-copying each second antenna connection vector and generating multiple third antenna connection vectors; performing mutation operations on each third antenna connection vector and generating multiple fourth satellite connection vectors; and repeating iterations until a termination condition is met, and outputting the optimal solution corresponding to the target optimization function, wherein the termination condition is used to indicate a preset number of iterations.

[0094] Therefore, according to this embodiment, the technical effect of reducing the time, space and resource costs consumed when the antenna array of the mobile object switches the connection beam is achieved.

[0095] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0096] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0100] If the integrated unit is implemented in the form of 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. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0101] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A beam switching prediction method for an antenna array, characterized in that: include: Determining a first position of a moving object in a next time period based on predetermined navigation information of the moving object, wherein the moving object is provided with an antenna array; Determine, according to the ephemeris information and the beam scheduling information of each first satellite, a plurality of beams connected to the mobile body at the first position of the mobile body in the next time period; Based on a preset neural network model, predicting a target beam that establishes a connection with the mobile object from the multiple beams; Based on an initialized population including a plurality of first antenna connection vectors corresponding to the antenna array, a corresponding antenna connection optimization function is generated, and an optimal solution of a target optimization function corresponding to the plurality of antenna connection optimization functions is determined by using a genetic algorithm, wherein the antenna connection optimization function is used to indicate the fitness of each antenna in the antenna array and the target beam connection, and the target optimization function is used to indicate the maximum fitness among the plurality of antenna connection optimization functions; as well as In case of determining the optimal solution of the target optimization function, predicting the target antenna connected to the target beam, wherein The operation of generating a corresponding antenna connection optimization function based on an initialization population including a plurality of first antenna connection vectors corresponding to the antenna array includes: Determine a plurality of influencing factors corresponding to each first antenna connection vector in the initialization population, wherein the influencing factors include beam coverage time, number of remaining channels, and signal quality; Determine the weight ratio corresponding to each influencing factor; and According to the multiple influencing factors and the weight ratios corresponding to the respective influencing factors, multiple antenna connection optimization functions corresponding to the respective first antenna connection vectors are determined, wherein The operation of determining the optimal solution of the target optimization function corresponding to the plurality of antenna connection optimization functions using a genetic algorithm includes: Based on the plurality of first antenna connection vectors, determining a plurality of second antenna connection vectors, wherein the second antenna connection vectors represent child-generation connection vectors of the plurality of first antenna connection vectors; Cross-copying each second antenna connection vector and generating a plurality of third antenna connection vectors; performing a mutation operation on each third antenna connection vector and generating a plurality of fourth satellite connection vectors; and The iteration is repeated until a termination condition is met, and an optimal solution corresponding to the target optimization function is output, wherein the termination condition is used to indicate a preset number of iterations.

2. The method according to claim 1, characterized in that: The operation of determining, according to the ephemeris information and the beam scheduling information of each first satellite, a plurality of beams connected to the mobile body at the first position of the mobile body in the next time period includes: Determine the second position of each of the first satellites in a next period of time according to the ephemeris information, and determine a plurality of second satellites corresponding to the mobile object based on the first position of the mobile object in the next period of time; and Based on the beam scheduling information of each second satellite, the beams corresponding to the second satellites in the next time period are determined, wherein the beams corresponding to the second satellites are used to indicate a plurality of beams for establishing a connection with the mobile object.

3. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 2.

4. A beam switching prediction device for an antenna array, characterized in that: include: A position determination module, used to determine a first position of a moving body in a next time period based on predetermined navigation information of the moving body, wherein the moving body is provided with an antenna array; A first beam determination module is used to determine, according to the ephemeris information and the beam scheduling information of each first satellite, a plurality of beams connected to the mobile body at the first position of the mobile body in the next time period; A beam prediction module, used for predicting a target beam connected to the mobile object from the multiple beams based on a preset neural network model; an optimal solution determination module, configured to generate a corresponding antenna connection optimization function based on an initialization population including a plurality of first antenna connection vectors corresponding to the antenna array, and to determine an optimal solution of a target optimization function corresponding to the plurality of antenna connection optimization functions by using a genetic algorithm, wherein the antenna connection optimization function is used to indicate a degree of fitness of each antenna in the antenna array and the target beam connection, and the target optimization function is used to indicate a maximum degree of fitness among the plurality of antenna connection optimization functions; as well as A target antenna prediction module is used to predict the target antenna connected to the target beam when the optimal solution of the target optimization function is determined, wherein The optimal solution determination module comprises: An influencing factor determination module, used to determine a plurality of influencing factors corresponding to each first antenna connection vector in the initialization population, wherein the influencing factors include beam coverage time, number of remaining channels, and signal quality; A weight ratio determination module, used to determine the weight ratio corresponding to each influencing factor; and An optimization function determination module is used to determine a plurality of antenna connection optimization functions corresponding to each of the first antenna connection vectors according to the plurality of influencing factors and the weight ratios corresponding to the respective influencing factors, wherein The optimal solution determination module comprises: A first antenna connection vector determining module, configured to determine a plurality of second antenna connection vectors based on the plurality of first antenna connection vectors, wherein the second antenna connection vectors represent a sub-generation connection vector of the plurality of first antenna connection vectors; A second antenna connection vector determination module, configured to cross-copy each second antenna connection vector and generate a plurality of third antenna connection vectors; a third antenna connection vector determination module, configured to perform a mutation operation on each third antenna connection vector and generate a plurality of fourth satellite connection vectors; and The iterative module is used to iterate repeatedly until a termination condition is met and output an optimal solution corresponding to the target optimization function, wherein the termination condition is used to indicate a preset number of iterations.

5. The device according to claim 4, characterized in that The first beam determination module includes: A second satellite determination module is used to determine the second position of each of the first satellites in the next period according to the ephemeris information, and determine a plurality of second satellites corresponding to the mobile body based on the first position of the mobile body in the next period; and The second beam determination module is used to determine the beams corresponding to the second satellites in the next time period based on the beam scheduling information of the second satellites, wherein the beams corresponding to the second satellites are used to indicate multiple beams for establishing connection with the mobile body.

6. A beam switching prediction device for an antenna array, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Determining a first position of a moving object in a next time period based on predetermined navigation information of the moving object, wherein the moving object is provided with an antenna array; Determine, according to the ephemeris information and the beam scheduling information of each first satellite, a plurality of beams connected to the mobile body at the first position of the mobile body in the next time period; Based on a preset neural network model, predicting a target beam that establishes a connection with the mobile object from the multiple beams; Based on an initialized population including a plurality of first antenna connection vectors corresponding to the antenna array, a corresponding antenna connection optimization function is generated, and an optimal solution of a target optimization function corresponding to the plurality of antenna connection optimization functions is determined by using a genetic algorithm, wherein the antenna connection optimization function is used to indicate the fitness of each antenna in the antenna array and the target beam connection, and the target optimization function is used to indicate the maximum fitness among the plurality of antenna connection optimization functions; as well as In case of determining the optimal solution of the target optimization function, predicting the target antenna connected to the target beam, wherein The operation of generating a corresponding antenna connection optimization function based on an initialization population including a plurality of first antenna connection vectors corresponding to the antenna array includes: Determine a plurality of influencing factors corresponding to each first antenna connection vector in the initialization population, wherein the influencing factors include beam coverage time, number of remaining channels, and signal quality; Determine the weight ratio corresponding to each influencing factor; and According to the multiple influencing factors and the weight ratios corresponding to the respective influencing factors, multiple antenna connection optimization functions corresponding to the respective first antenna connection vectors are determined, wherein The operation of determining the optimal solution of the target optimization function corresponding to the plurality of antenna connection optimization functions using a genetic algorithm includes: Based on the plurality of first antenna connection vectors, determining a plurality of second antenna connection vectors, wherein the second antenna connection vectors represent child-generation connection vectors of the plurality of first antenna connection vectors; Cross-copying each second antenna connection vector and generating a plurality of third antenna connection vectors; performing a mutation operation on each third antenna connection vector and generating a plurality of fourth satellite connection vectors; and The iteration is repeated until a termination condition is met, and an optimal solution corresponding to the target optimization function is output, wherein the termination condition is used to indicate a preset number of iterations.

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