Channel prediction method and device for low-altitude network, equipment and storage medium
By collecting channel quality parameters in a low-altitude network and using the Markov chain model for channel prediction and pre-scheduling, the technical problems of low-altitude drone channel prediction are solved, and the channel guarantee and user experience of drone services are improved.
Patent Information
- Application Number
- CN202311801195.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-25
- Publication Date
- 2025-06-27
AI Technical Summary
The lack of a complete solution for channel prediction for low-altitude drones in the prior art, resulting in low-altitude network complexity problems, affecting users' video fluency and clarity, and thus limiting the widespread development of 5G networked drone services.
By collecting channel quality parameters, such as signal-to-noise ratio, signal reception power and signal reception quality, using the Markov chain model, the status probability and state transition probability of channel quality parameters are calculated, and the status of the channel quality parameters at the next moment is estimated, and the corresponding request instructions are sent to the base station for pre-scheduling.
It realizes prediction and pre-scheduling of low-altitude network channel status, improves channel guarantee for drone services, and improves user experience.
Smart Images
Figure CN120224459A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wireless mobile communication technologies, and in particular, to a channel prediction method, apparatus, device, and storage medium for a low-altitude network. Background Art
[0002] A Markov chain consists of a set of states and the transition probabilities between the states. The states can be discrete or continuous. The transition probability describes the probability of transitioning from one state to another state. That is, the core idea is to predict future states or understand the behavior of the system by calculating the state transition probabilities. As the leading industry of China's low-altitude economy, drones are in a stage of continuous development and iteration. Currently, in the development of low-altitude drone services, due to the complexity of the low-altitude network, it is impossible to provide users with a good service experience. For example, the experience in terms of video smoothness, video clarity, etc. Therefore, the 5G-connected drone service has not been widely developed.
[0003] However, there is no complete solution for channel prediction of low-altitude drones in the prior art. Summary of the Invention
[0004] A channel prediction method, apparatus, device, and storage medium for a low-altitude network are provided to solve the technical problem of channel prediction for low-altitude drones.
[0005] In a first aspect, this application provides a channel prediction method for a low-altitude network, including:
[0006] Collecting channel quality parameters, where the channel quality parameters include signal-to-noise ratio, signal reception power, and signal reception quality;
[0007] Based on the channel quality parameters and the state threshold intervals of the channel quality parameters, obtaining the state probabilities of the channel quality parameters in different state threshold intervals, and the state transition probabilities from the current state threshold interval to other state threshold intervals;
[0008] Based on the state probabilities and state transition probabilities of the channel quality parameters, estimating the state of the channel quality parameters of the terminal at the next moment;
[0009] According to the state of the channel quality parameters at the next moment, sending a corresponding request instruction to the base station.
[0010] In a second aspect, this application provides a channel prediction apparatus for a low-altitude network, including:
[0011] A collection module, configured to collect channel quality parameters, where the channel quality parameters include signal-to-noise ratio, signal reception power, and signal reception quality;
[0012] A calculation module, configured to obtain the state probabilities of the channel quality parameter in different state threshold intervals and the state transition probabilities from the current state threshold interval to other state threshold intervals according to the channel quality parameter and the state threshold intervals of the channel quality parameter;
[0013] A prediction module, configured to estimate the state of the channel quality parameter of the terminal at the next moment according to the state probabilities and state transition probabilities of the channel quality parameter;
[0014] A sending module, configured to send a corresponding request instruction to the base station according to the state of the channel quality parameter at the next moment.
[0015] In a third aspect, the present application provides an electronic device, including: a processor and a memory communicatively connected to the processor;
[0016] The memory stores computer-executable instructions;
[0017] The processor executes the computer-executable instructions stored in the memory to execute the channel prediction method for the low-altitude network of the present application.
[0018] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the channel prediction method for the low-altitude network of the present application.
[0019] The channel prediction method for the low-altitude network provided by the present application, through the collected channel quality parameters, according to the state probabilities when the channel quality parameters are in excellent, good, and poor states respectively, obtains the state transition matrix of the channel quality parameter through a Markov chain, and further can determine the state of the channel quality parameter of the terminal at the next moment. The channel resources are pre-scheduled according to the state of the channel quality parameter at the next moment, realizing the combination of channel state prediction and the UAV service of the low-altitude network. The pre-scheduling of channel resources provides better channel guarantee for the UAV service and improves the user experience. Description of the Drawings
[0020] The drawings here are incorporated into the specification and constitute a part of this specification, showing the embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.
[0021] Figure 1 It is a schematic diagram of the scenario of the channel prediction method for the low-altitude network provided by the embodiment of the present application;
[0022] Figure 2 It is a schematic flowchart of the channel prediction method for the low-altitude network provided by the embodiment of the present application;
[0023] Figure 3Schematic flowchart of the method for calculating the state transition matrix of channel quality parameters provided by the embodiments of the present application;
[0024] Figure 4 Schematic structural diagram of the channel prediction device for the low-altitude network provided by the embodiments of the present application;
[0025] Figure 5 Schematic structural diagram of the electronic device provided by the embodiments of the present application.
[0026] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed implementation manners
[0027] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of the devices and methods consistent with some aspects of the present application as detailed in the appended claims.
[0028] In the prior art, unmanned aerial vehicles (UAVs) have been widely used due to their flexible maneuverability and wide moving range, and have received the attention of modern communication technologies. In UAV communication, the requirements for communication quality in new application scenarios are also increasing day by day. Currently, problems such as channel gain prediction, wireless relay planning, and source localization between ground terminals and low-altitude UAVs need to be solved. In the existing solutions, a UAV ground-air channel prediction method based on a virtual geometric environment is proposed. By constructing a ground-air UAV channel gain prediction map, a reliable basis is provided for UAV communication. The purpose of this solution is to improve the accuracy of channel prediction. However, due to the complexity of the low-altitude network, there are still situations where the video fluency and clarity are not good when performing UAV services, resulting in an unsatisfactory user experience.
[0029] A channel prediction method for a low-altitude network provided by the embodiments of the present application combines the Markov chain, combines the real-time state of the channel with the capabilities of the communication module, determines the state of the channel quality parameters at the next moment according to the state probabilities when the channel quality parameters are in the excellent, good, and poor states respectively, and then pre-schedules the channel resources in advance, providing better channel guarantee for UAV services and improving the user experience.
[0030] A channel prediction method for a low-altitude network provided by the present application aims to solve the above technical problems in the prior art.
[0031] The following uses specific embodiments to elaborate in detail on the technical solution of the present application and how the technical solution of the present application solves the above technical problems. The following several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.
[0032] Figure 1 This is a schematic diagram of the scenario of the channel prediction method for the low-altitude network provided by the embodiments of the present application. As Figure 1 shown, the execution subject of the channel prediction method provided by the embodiments of the present application is the server. By predicting the state of the channel quality parameters at the next moment through the server, the pre-interaction with the base station is completed. The implementation manner of the execution subject in this embodiment is not particularly limited, and the server can be implemented in a software manner, a hardware manner, or a combination of hardware and software.
[0033] Figure 2 This is a flowchart of the channel prediction method for the low-altitude network provided by the embodiments of the present application.
[0034] As Figure 2 shown, the method may include:
[0035] S201. Collect channel quality parameters, where the channel quality parameters include signal-to-noise ratio, signal reception power, and signal reception quality.
[0036] Among them, the channel quality parameter refers to a performance index used to describe the current wireless channel.
[0037] Among them, the signal-to-noise ratio (SINR) refers to the ratio of the intensity of the received useful signal to the intensity of the received interference signal (noise and interference), with the unit of dB, and the general value range can be between -23 dB and 40 dB; the received signal power (RSRP) refers to a key parameter that can represent the wireless signal intensity in the network, which is the average value of the signal power received on all resource particles carrying the reference signal within a certain symbol. Here, the symbol refers to the modulated signal, and the power value of the RSRP represents the power value of each subcarrier, with the unit of dBm, and the general value range can be between -44 dBm and -140 dBm; the received signal quality (RSRQ) refers to the reference signal reception quality, which is used for the measurement handover and cell reselection scenarios of mobile phone users, and sorts different candidate cells according to the signal quality, with the unit of dB, and the general value range can be between -43 dB and 20 dB.
[0038] S202. According to the channel quality parameters and the state threshold intervals of the channel quality parameters, obtain the state probabilities of the channel quality parameters in different state threshold intervals, and the state transition probabilities from the current state threshold interval to other state threshold intervals.
[0039] Among them, the state threshold interval of the channel quality parameter refers to a parameter value interval that can determine the channel performance preset according to historical channel quality parameters. For example, the parameter value intervals can be excellent, good, and poor.
[0040] Among them, the state probability of the channel quality parameter refers to the probability that the value of the collected channel quality parameter is in different interval states.
[0041] Among them, the state transition matrix refers to the state transition matrix represented based on the Markov chain. The elements of the state transition matrix are the probabilities that the value of the channel quality parameter changes from the current value interval to the next value interval.
[0042] Since the larger the values of the signal-to-noise ratio, signal reception power, and signal reception quality in the channel quality parameter are, the better, that is, the larger the value, the better the current channel quality. According to the collected historical channel quality parameters, determining the state threshold interval of the channel quality parameter includes:
[0043] The state threshold interval of the signal-to-noise ratio can be, for example: when the parameter value interval is [40, 15), the state is excellent; when the parameter value interval is [15, -5), the state is good; when the parameter value interval is [-5, -23], the state is poor.
[0044] The state threshold interval of the signal reception power can be, for example: when the parameter value interval is [-44, -80), the state is excellent; when the parameter value interval is [-80, -100), the state is good; when the parameter value interval is [-100, -140], the state is poor.
[0045] The state threshold interval of the signal reception quality can be, for example: when the parameter value interval is [20, 0), the state is excellent; when the parameter value interval is [0, -20), the state is good; when the parameter value interval is [-20, -43], the state is poor.
[0046] S203. Estimate the state of the channel quality parameter of the terminal at the next moment according to the state probability and state transition probability of the channel quality parameter.
[0047] For example, when the drone is running, the probabilities of the collected signal-to-noise ratio being in good, good, and poor states at time T1 are (Pa, Pb, Pc), where the probabilities of good, good, and poor need to satisfy Pa + Pb + Pc = 1. The state transition matrix of the current service scenario is It can be obtained that the probabilities of the signal-to-noise ratio being in good, good, and poor states at time T2 are:
[0048]
[0049] Similarly, the probabilities of the signal reception power and signal reception quality being good, fair, and poor at time T2 can be calculated respectively, and then the current signal-to-noise ratio, signal reception power, and signal reception quality status can be determined.
[0050] It should be noted that according to the state probability and state transition probability of the channel quality parameter, the state of the channel quality parameter of the terminal at the next moment is obtained, including:
[0051] Multiply the state probability of the channel quality parameter by the state transition matrix of the channel quality parameter to obtain the state probability of the channel quality parameter of the terminal at the next moment;
[0052] Determine the state corresponding to the maximum value in the state probability of the channel quality parameter as the state of the channel quality parameter of the terminal at the next moment.
[0053] For example, when the probabilities of the signal-to-noise ratio being good, fair, and poor at time T1 are (0.5, 0.4, 0.1), and the state transition matrix of the current service scenario is Multiplying the two gives the probabilities of the signal-to-noise ratio being good, fair, and poor at time T2 as (0.32, 0.47, 0.21). Among them, 0.47 is the maximum value, so it is determined that the state of the signal-to-noise ratio at time T2 is fair. Similarly, the states of the signal reception power and signal reception quality at time T2 can be obtained respectively.
[0054] S204. Send a corresponding request instruction to the base station according to the state of the channel quality parameter at the next moment.
[0055] According to the state of the channel quality parameter at the next moment, judge the quality of the current overall communication channel, complete the pre-interaction with the base station. If the current channel quality is developing in a good direction, more network resources can be requested in advance. If the current channel quality is developing in a bad direction, the current channel can be released in advance and switched to the next base station.
[0056] It should be noted that sending a corresponding request instruction to the base station according to the state of the channel quality parameter at the next moment includes:
[0057] Determine the state of the channel quality parameter of different types at the next moment. The state includes a first state, a second state, and a third state corresponding to multiple state threshold intervals in descending order according to the threshold;
[0058] Send different request instructions to the base station according to the proportion of the first state, the second state, and the third state.
[0059] For example, the general value range of the signal-to-noise ratio can be [40, -23]. According to the thresholds from large to small, it corresponds to the first state, the second state, and the third state of multiple state threshold intervals in sequence. That is, the state threshold interval of [40, 15) indicates that the first state is excellent, the state threshold interval of [15, -5) indicates that the second state is good, and the state threshold interval of [-5, -23] indicates that the third state is poor; similarly, the first state, the second state, and the third state of the signal reception power and the signal reception quality can be obtained.
[0060] It should be noted that when all the states are the first state, or only contain the first state and the second state, a request instruction for reserving channel resources is sent to the base station; when all the states are the second state, or one of the states is the third state and the other states are all the second state, a request instruction for not reserving channel resources is sent to the base station; when at least two of the states are the third state, a request instruction for switching the base station and a request instruction for not reserving channel resources are sent to the base station.
[0061] According to the obtained signal-to-noise ratio, signal reception power, and signal reception quality states, the current channel quality is judged. For example, when the states of SINR, RSRP, and RSRQ at time T2 are good, good, and excellent respectively, it indicates that the overall channel quality is developing in a good trend. The terminal sends a request instruction for reserving channel resources to the nearest base station, requesting to reserve more channel resources. The base station responds to the request instruction sent by the terminal, improves the priority of the current communication link, and releases more resource blocks; when the states of SINR, RSRP, and RSRQ at time T2 are all good, or two of them are good and one of them is poor, the terminal requests the base station not to reserve channel resources; when the states of SINR, RSRP, and RSRQ at time T2 include two or less poor situations, the terminal proactively initiates a cell handover request in advance. The base station responds to the request instruction sent by the terminal, releases this connection in advance, and instructs the terminal to switch to the next base station.
[0062] In summary, in the embodiment of the present application, according to the state probabilities of the channel quality parameters in the excellent, good, and poor states respectively, after obtaining the state transition matrix, the state of the channel quality parameters of the terminal at the next moment is determined, the channel resources are pre-scheduled, and the channel resource preparation is made in advance according to the current channel quality, providing an overall system solution for the channel prediction of low-altitude unmanned aerial vehicles, providing better channel guarantee for unmanned aerial vehicle services, and improving the user experience.
[0063] On the basis of the above embodiments, through the following embodiments, the implementation manners of the method for obtaining the state probability and the state transition matrix of the channel quality parameters according to the channel quality parameters and the state threshold intervals of the channel quality parameters are further described.
[0064] Figure 3This is a schematic flowchart of a method for calculating the state transition matrix of channel quality parameters provided by an embodiment of the present application. As Figure 3 shown, the method includes:
[0065] S301. Determine multiple state threshold intervals for the states of the channel quality parameters;
[0066] S302. According to the historical parameter data of the channel quality parameters and the multiple state threshold intervals, respectively obtain the probabilities of the states of the channel quality parameters in different state threshold intervals.
[0067] Among them, according to the states of the channel quality parameters including excellent, good, and poor, calculate the probabilities that the values of the signal-to-noise ratio in the historical parameter data are respectively in the intervals [40, 15), [15, -5), [-5, -23], the probabilities that the values of the signal reception power are respectively in the intervals [-44, -80), [-80, -100), [-100, -140], and the probabilities that the values of the signal reception quality are respectively in the intervals [20, 0), [0, -20), [-20, -43].
[0068] S303. Determine the state transition matrix according to the probabilities in different state threshold intervals, and the elements of the state transition matrix are the state transition probabilities from the current state threshold interval to other state threshold intervals.
[0069] For example, when the signal-to-noise ratio changes from the excellent state to the next states of excellent, good, and poor, the probabilities are P1, P2, and P3 respectively; when the signal reception power changes from the good state to the next states of excellent, good, and poor, the probabilities are P4, P5, and P6 respectively; when the signal reception quality changes from the poor state to the next states of excellent, good, and poor, the probabilities are P7, P8, and P9 respectively. That is, the state transition matrix of the signal-to-noise ratio is expressed as Similarly, the state transition matrices of the signal reception power and the signal reception quality can be obtained.
[0070] It should be noted that determining the state transition matrix according to the probabilities in different state threshold intervals includes:
[0071] Determine the initial state transition probabilities according to the probabilities in different state threshold intervals;
[0072] According to the currently collected channel quality parameters and the multiple state threshold intervals, obtain the state probabilities of the currently collected channel quality parameters;
[0073] According to the state probabilities of the currently collected channel quality parameters and the initial state transition probabilities, obtain the state transition probabilities;
[0074] A state transition matrix is formed according to the state transition probabilities. The state transition matrix obtained from the historical parameter data of the channel quality parameters collected by the terminal in the early stage is configured in the module of the terminal as the initial state transition matrix. When the terminal enters the service scenario in City 1, the terminal is set to the inactivity state (rest state) in the first few flight missions. The initial state transition matrix is corrected according to the changes in the collected SINR, RSRP, and RSRQ. Among them, the inactivity state can indicate that the terminal is in a state of only collecting channel quality parameters and not predicting the channel quality.
[0075] In the embodiments of the present application, after the state transition matrix is corrected in the service scenario in City 1, it can be used as the initial state transition matrix in the service scenarios of other cities, achieving the purpose of quickly putting the module into application and improving the user experience.
[0076] Figure 4 This is a schematic diagram of the channel prediction device for the low-altitude network provided by the embodiments of the present application. As Figure 4 shown, the channel prediction device 400 for the low-altitude network includes: a collection module 401, a calculation module 402, a prediction module 403, and a sending module 404.
[0077] The collection module 401 is used to collect channel quality parameters, and the channel quality parameters include signal-to-noise ratio, signal reception power, and signal reception quality;
[0078] The calculation module 402 is used to obtain the state probabilities of the channel quality parameters in different state threshold intervals and the state transition probabilities from the current state threshold interval to other state threshold intervals according to the channel quality parameters and the state threshold intervals of the channel quality parameters;
[0079] The prediction module 403 is used to estimate the state of the channel quality parameters of the terminal at the next moment according to the state probabilities and state transition probabilities of the channel quality parameters;
[0080] The sending module 404 is used to send a corresponding request instruction to the base station according to the state of the channel quality parameters at the next moment.
[0081] Among them, in the embodiments of the present application, the calculation module 402 further includes:
[0082] Determine multiple state threshold intervals for the state of the channel quality parameters;
[0083] According to the historical parameter data of the channel quality parameters and multiple state threshold intervals, respectively obtain the probabilities of the state of the channel quality parameters in different state threshold intervals;
[0084] Determine the state transition matrix according to the probabilities of different state threshold intervals, where the elements of the state transition matrix are the state transition probabilities from the current state threshold interval to other state threshold intervals.
[0085] Among them, in the embodiment of the present application, the calculation module 402 further includes:
[0086] Determine the initial state transition probability according to the probabilities of different state threshold intervals;
[0087] Obtain the state probability of the current channel quality parameter according to the currently collected channel quality parameter and multiple state threshold intervals;
[0088] Obtain the state transition probability according to the state probability of the current channel quality parameter and the initial state transition probability;
[0089] Form the state transition matrix according to the state transition probabilities.
[0090] Among them, in the embodiment of the present application, the prediction module 403 further includes:
[0091] Multiply the state probability of the channel quality parameter by the state transition matrix of the channel quality parameter to obtain the state probability of the channel quality parameter of the terminal at the next moment;
[0092] Determine the state corresponding to the maximum value in the state probability of the channel quality parameter as the state of the channel quality parameter of the terminal at the next moment.
[0093] Among them, in the embodiment of the present application, the sending module 404 further includes:
[0094] Determine the states of the channel quality parameters of different types at the next moment, where the states include a first state, a second state, and a third state corresponding to multiple state threshold intervals in descending order of thresholds;
[0095] Send different request instructions to the base station according to the proportion of the first state, the second state, and the third state.
[0096] Among them, in the embodiment of the present application, the sending module 404 further includes:
[0097] When all the states are the first state, or only include the first state and the second state, send a request instruction to the base station to reserve channel resources.
[0098] Among them, in the embodiment of the present application, the sending module 404 further includes:
[0099] When all the states are the second state, or one of the states is the third state and the other states are all the second state, send a request instruction to the base station not to reserve channel resources.
[0100] Among them, in the embodiments of the present application, the sending module 404 further includes:
[0101] When at least two third states are included in the status, a request instruction for switching the base station and a request instruction for not reserving channel resources are sent to the base station.
[0102] In summary, the channel prediction device of the low-altitude network provided in this embodiment collects channel quality parameters through the collection module 401, and then calculates the state probability and state transition probability when the channel quality parameters are in the excellent, good, and poor states according to the calculation module 402. Furthermore, the prediction module 403 can predict the state of the channel quality parameters at the next moment, and finally, the corresponding request instruction is sent through the sending module 404 to pre-schedule the channel resources, improving the channel guarantee in the UAV service and the user experience. It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, 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 optional embodiments, and the actions and modules involved are not necessarily essential to the present application.
[0103] Furthermore, it should be noted that although the steps in the flowchart are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.
[0104] It should be understood that the above device embodiments are illustrative, and the devices of the present application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.
[0105] Figure 5 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. As Figure 5 shown, the electronic device 500 includes:
[0106] The electronic device 500 may include components such as a processor 501 with one or more processing cores, a memory 502 with one or more computer-readable storage media, and a communication component 503. Among them, the processor 501, the memory 502, and the communication component 503 are connected through a bus 504.
[0107] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the message processing method as described above.
[0108] For the specific implementation process of the processor 501, reference can be made to the above method embodiments. Their implementation principles and technical effects are similar, and will not be elaborated here in this embodiment.
[0109] In the above Figure 5 In the illustrated embodiment, it should be understood that the processor may be a central processing unit (Central Processing Unit, CPU for short), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP for short), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the present application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.
[0110] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.
[0111] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.
[0112] Other embodiments of the present application will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.
[0113] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.
Claims
1. A channel prediction method for a low-altitude network, characterized in that Including: Collecting channel quality parameters, where the channel quality parameters include signal-to-noise ratio, signal reception power, and signal reception quality; Based on the channel quality parameters and the state threshold intervals of the channel quality parameters, obtaining the state probabilities of the channel quality parameters in different state threshold intervals, and the state transition probabilities from the current state threshold interval to other state threshold intervals; Based on the state probabilities and state transition probabilities of the channel quality parameters, predicting the state of the channel quality parameters of the terminal at the next moment; Based on the state of the channel quality parameters at the next moment, sending a corresponding request instruction to the base station.
2. The method according to claim 1, characterized in that The step of obtaining the state probabilities of the channel quality parameters in different state threshold intervals, and the state transition probabilities from the current state threshold interval to other state threshold intervals based on the channel quality parameters and the state threshold intervals of the channel quality parameters includes: Determining multiple state threshold intervals of the state of the channel quality parameters; Based on the historical parameter data of the channel quality parameters and the multiple state threshold intervals, respectively obtaining the probabilities of the state of the channel quality parameters in different state threshold intervals; Determining a state transition matrix based on the probabilities of the different state threshold intervals, where the elements of the state transition matrix are the state transition probabilities from the current state threshold interval to other state threshold intervals.
3. The method according to claim 2, characterized in that, The step of determining the state transition matrix based on the probabilities of the different state threshold intervals includes: Determining the initial state transition probabilities based on the probabilities of the different state threshold intervals; Based on the currently collected channel quality parameters and the multiple state threshold intervals, obtaining the state probability of the currently collected channel quality parameters; Based on the state probability of the currently collected channel quality parameters and the initial state transition probabilities, obtaining the state transition probabilities; Forming the state transition matrix based on the state transition probabilities.
4. The method according to claim 1 or 2, characterized in that, The step of obtaining the state of the channel quality parameters of the terminal at the next moment based on the state probabilities and state transition probabilities of the channel quality parameters includes: Multiplying the state probabilities of the channel quality parameters and the state transition matrix of the channel quality parameters to obtain the state probability of the channel quality parameters of the terminal at the next moment; Determining the state corresponding to the maximum value in the state probabilities of the channel quality parameters as the state of the channel quality parameters of the terminal at the next moment.
5. The method according to claim 1, wherein The step of sending a corresponding request instruction to the base station based on the state of the channel quality parameters at the next moment includes: Determining the states of the channel quality parameters of different types at the next moment, where the states include a first state, a second state, and a third state corresponding to multiple state threshold intervals in descending order of thresholds; Based on the proportion of the first state, the second state, and the third state, sending different request instructions to the base station.
6. The method according to claim 5, characterized in that, The step of sending different request instructions to the base station based on the proportion of the first state, the second state, and the third state includes: When all the states are the first state, or only include the first state and the second state, sending a request instruction for reserving channel resources to the base station.
7. The method according to claim 5, wherein Sending different request instructions to the base station according to the proportion of the first state, second state, and third state, including: When all of the states are the second state, or one of the states is the third state and the other states are all the second state, sending a request instruction for not reserving channel resources to the base station.
8. The method according to claim 5, wherein Sending different request instructions to the base station according to the proportion of the first state, second state, and third state, including: When at least two of the states are the third state, sending a request instruction for switching the base station and a request instruction for not reserving channel resources to the base station.
9. A channel prediction device for a low-altitude network, characterized in that, Including: An acquisition module, configured to acquire channel quality parameters, where the channel quality parameters include signal-to-noise ratio, signal reception power, and signal reception quality; A calculation module, configured to obtain the state probability that the channel quality parameter is in different state threshold intervals and the state transition probability of changing from the current state threshold interval to other state threshold intervals according to the channel quality parameter and the state threshold interval of the channel quality parameter; A prediction module, configured to estimate the state of the channel quality parameter of the terminal at the next moment according to the state probability and state transition probability of the channel quality parameter; A sending module, configured to send a corresponding request instruction to the base station according to the state of the channel quality parameter at the next moment.
10. An electronic device, characterized in that, Including: A processor and a memory communicatively connected to the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method according to any one of claims 1 to 8.
11. A computer-readable storage medium, characterized in that, Computer execution instructions are stored in the computer-readable storage medium, and when the computer execution instructions are executed by the processor, they are used to implement the method according to any one of claims 1 to 8.