Method and device for reducing influence of rain attenuation on communication link of satellite gateway station

Abstract

The invention discloses a method and device for reducing influence of rain attenuation on a communication link of a satellite gateway station, and belongs to the field of satellite communication. According to the method, through historical multi-round interaction data of a gateway station and a satellite, including transmitting / receiving power, frequency and position information of two communication parties, time-space coupled signal characteristics including rain attenuation influences are extracted in real time, then a dual-target optimization deep learning model is adopted, a power compensation value and a frequency compensation value are synchronously predicted, and the real-time performance of the satellite is improved. And finally, according to the power compensation value and the frequency compensation value, determining the transmitting power and the transmitting frequency of the signal transmitted to the satellite by the gateway station in the Nth round so as to ensure that the gateway station transmits the signal to the satellite at the optimal transmitting power and the optimal transmitting frequency in the Nth round of communication, thereby dynamically compensating the rain attenuation of the link. The technical problems that in the prior art, a satellite gateway station rain attenuation resisting scheme excessively depends on meteorological data, only the signal power is adjusted, hysteresis exists in the response speed, and limitation exists in the compensation efficiency are solved.

Description

Technical Field

[0001] The present application relates to the field of satellite communication technology, and in particular to a method and device for reducing the impact of rain attenuation on satellite gateway communication links. Background Art

[0002] In satellite communication systems, rain attenuation is one of the core environmental factors that degrade link performance. When radio waves pass through rainfall, the absorption and scattering of electromagnetic waves by raindrops causes a sharp attenuation of signal power. This attenuation is significantly frequency-dependent (for example, Ka-band attenuation can be over 10 times that of C-band) and spatially variable (the median annual rain attenuation in tropical rainforests exceeds 30dB, while in deserts it is less than 5dB). The signal degradation caused by rain attenuation can lead to a deterioration in the carrier-to-noise ratio (C / N) and an increase in the bit error rate (BER). In extreme cases, it can cause communication link interruption, seriously threatening the availability and service quality of satellite communication systems.

[0003] Therefore, when designing and maintaining satellite communication systems, it is necessary to consider the impact of rain attenuation and take corresponding technical measures to reduce the impact of rain attenuation on communications. Existing anti-rain attenuation solutions generally adopt the technical path of "attenuation prediction based on meteorological data-unidimensional power adjustment", for example, the publication number is CN118038290A, and the invention name is a method and system for collaborative anti-rain attenuation based on a deep learning feature framework. It needs to rely on ground meteorological station data to drive compensation decisions; for example, the publication number is CN117692081A, and the invention name is a method and system for predicting rainfall attenuation in satellite communications. It also relies on meteorological data to drive compensation decisions. However, the existing anti-rain attenuation solutions have the following defects:

[0004] (1) Inherent delay in meteorological data links: Existing solutions rely heavily on ground-based meteorological stations or satellite-borne rainfall monitoring equipment to obtain real-time rainfall rate data. This technical architecture suffers from triple delays: data acquisition delay: the sampling interval of meteorological sensors is usually ≥1 minute, which cannot capture the sudden characteristics of heavy rain (the time scale of sudden changes in rainfall intensity is ≤10 seconds); information transmission delay: the return delay of meteorological data from the gateway to the satellite is hundreds of milliseconds (especially in GEO satellite scenarios); model calculation delay: the attenuation calculation based on models such as ITU-R P.618 requires iterative calculations, further increasing processing delays. This cumulative delay causes compensation actions to always lag behind the actual attenuation, increasing the risk of link interruption in heavy rain scenarios by more than 40%.

[0005] (2) Compensation limitations of single parameter adjustment: According to Mie scattering theory, the rain attenuation intensity has a nonlinear relationship with the ratio (λ / D) of the electromagnetic wave wavelength (λ) and the raindrop equivalent diameter (D): when the signal frequency (carrier frequency) increases, causing the wavelength to shorten to the order of raindrop size (such as Ka band λ ≈ 1 cm, D ≈ 2-3 mm), the rain attenuation intensity increases exponentially (α ∝ f 2.6 This spectrum-selective attenuation can cause a carrier-to-noise (C / N) loss of up to 10dB in fixed-frequency operation during heavy rainfall. However, existing solutions employ a fixed-frequency operation mode and only offer power compensation mechanisms, failing to dynamically adjust the signal frequency.

[0006] Currently, no effective solution has been proposed to address the technical issues that the existing satellite gateway anti-rain fade solutions in the above-mentioned prior art are over-reliance on meteorological data and only adjust signal power, resulting in a lag in response speed and limitations in compensation efficiency. Summary of the Invention

[0007] Embodiments of the present disclosure provide a method and apparatus for reducing the effects of rain fade on satellite gateway communication links, thereby at least resolving the technical issues in existing satellite gateway anti-rain fade solutions, such as over-reliance on meteorological data and limited adjustment of signal power, hysteresis in response speed, and limitations in compensation efficiency.

[0008] According to one aspect of an embodiment of the present disclosure, a method for reducing the impact of rain attenuation on a satellite gateway communication link is provided, comprising: determining gateway signal information, satellite feedback signal information, gateway location information, and satellite location information for rounds Nk to N-1 before the gateway sends the Nth round of signals to the satellite; wherein the gateway signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in round Ni, and the receive power and receive frequency of the gateway signal received by the satellite in round Ni; the satellite feedback signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the satellite to the gateway in round Ni, and the receive power and receive frequency of the gateway signal received by the satellite in round Ni. The receiving power and receiving frequency of the satellite signal received by the gateway in the N-th round; i=1-k; constructing a signal feature series based on the gateway signal information, satellite feedback signal information, gateway position information and satellite position information from the N-th round to the N-th round; wherein the signal feature series is composed of the signal features from the N-th round to the N-th round; using a pre-trained deep learning model, analyzing the signal feature series to determine the power compensation value and frequency compensation value of the signal transmitted by the gateway to the satellite in the N-th round; and determining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-th round based on the power compensation value and the frequency compensation value.

[0009] 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 run, a processor executes any one of the above methods.

[0010] According to another aspect of an embodiment of the present disclosure, there is also provided an apparatus for reducing the influence of rain attenuation on a satellite gateway communication link, comprising: an information determination module, configured to determine gateway signal information, satellite feedback signal information, gateway position information, and satellite position information for rounds Nk to N-1 before the gateway sends the Nth round of signals to the satellite; wherein the gateway signal information for round Ni includes the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in round Ni, and the receiving power and receiving frequency of the gateway signal received by the satellite in round Ni; the satellite feedback signal information for round Ni includes the transmission power and transmission frequency of the signal transmitted by the satellite to the gateway in round Ni, and the receiving power and receiving frequency of the gateway signal received by the satellite in round Ni. the receiving power and receiving frequency of the satellite signal received; i=1~k; a feature determination module, configured to construct a signal feature series based on the gateway station signal information, satellite feedback signal information, gateway station position information and satellite position information from the Nk-th round to the N-1-th round; wherein the signal feature series is composed of the signal features from the Nk-th round to the N-1-th round; a compensation value determination module, configured to analyze the signal feature series using a pre-trained deep learning model to determine the power compensation value and frequency compensation value of the signal transmitted by the gateway station to the satellite in the N-th round; and a power and frequency determination module, configured to determine the transmission power and transmission frequency of the signal transmitted by the gateway station to the satellite in the N-th round based on the power compensation value and the frequency compensation value.

[0011] According to another aspect of an embodiment of the present disclosure, an apparatus for reducing the impact of rain attenuation on a satellite gateway communication link is provided, comprising: a processor; and a memory, connected to the processor, for providing the processor with instructions for processing the following processing steps: determining, before the gateway sends an Nth round of signals to the satellite, gateway signal information, satellite feedback signal information, gateway location information, and satellite location information for rounds Nk to N-1; wherein the gateway signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in round Ni, and the receive power and receive frequency of the gateway signal received by the satellite in round Ni; the satellite feedback signal information for round Ni includes the information received by the satellite in round Ni to the gateway The present invention relates to a method for determining a transmission power and a transmission frequency of a signal transmitted by a gateway, and a reception power and a reception frequency of a satellite signal received by the gateway in the N-th round; i=1-k; constructing a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information and satellite location information from the Nk-th round to the N-1-th round; wherein the signal feature series is composed of signal features from the Nk-th round to the N-1-th round; analyzing the signal feature series using a pre-trained deep learning model to determine a power compensation value and a frequency compensation value of a signal transmitted by the gateway to the satellite in the N-th round; and determining the transmission power and the transmission frequency of the signal transmitted by the gateway to the satellite in the N-th round based on the power compensation value and the frequency compensation value.

[0012] Before the gateway sends its Nth round of signals to the satellite, the control center within the gateway determines gateway signal information, satellite feedback signal information, gateway location information, and satellite position information for rounds Nk through N-1. By determining the various signal parameters (transmit / receive power, frequency) and position information of the communication link itself, this approach enables real-time self-extraction and predictive perception of rain attenuation characteristics, providing a forward-looking basis for subsequent compensation strategies. The control center then constructs a signal feature set based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite position information for rounds Nk through N-1. This eliminates the need to wait for ground-based meteorological station data (delay ≥ 1 minute) and instead directly extracts spatiotemporally coupled attenuation characteristics (end-to-end delay < 10ms) from the gateway-satellite interaction signal and position information. This establishes an autonomous decision-making system that is independent of external data and lays the foundation for real-time self-triggering of rain attenuation responses. Secondly, the control center uses a pre-trained deep learning model to analyze the signal feature series and determine the power and frequency compensation values ​​for the signal transmitted by the gateway to the satellite in the Nth round. This model simultaneously achieves power-frequency coordinated prediction through dual-objective optimization, providing precise adjustment instructions for subsequent dual power and frequency adjustments. Finally, based on the power and frequency compensation values, the control center determines the transmit power and frequency of the signal transmitted by the gateway to the satellite in the Nth round. This ensures that during the Nth round of communication, the gateway transmits signals to the satellite at the optimal transmit power and frequency, thereby dynamically compensating for rain attenuation in the link and maintaining the signal in a suitable operating state. This addresses the technical issues of existing satellite gateway anti-rain attenuation solutions, which rely excessively on meteorological data and only adjust signal power, resulting in lags in response speed and limitations in compensation effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of this 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 of the present disclosure. In the drawings:

[0014] Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to embodiment 1 of the present disclosure;

[0015] Figure 2 1 is a schematic diagram of the hardware architecture of a system for reducing the impact of rain attenuation on a satellite gateway communication link according to Embodiment 1 of the present disclosure;

[0016] Figure 3 is a flowchart of the method for reducing the impact of rain attenuation on a satellite gateway communication link according to Example 1 of the present application;

[0017] Figure 4 1 is a schematic diagram of the communication process between the satellite and the gateway according to Example 1 of the present application;

[0018] Figure 5 2 is a schematic diagram of a message sent by a gateway to a satellite in round N-1 according to Example 1 of the present application;

[0019] Figure 6 This is a schematic diagram of a message sent by a satellite to a gateway in round N-1 according to Example 1 of the present application;

[0020] Figure 7 2 is a schematic diagram of a message sent by a gateway to a satellite in the Nth round according to Example 1 of the present application;

[0021] Figure 8 2 is a schematic diagram of a message sent by a satellite to a gateway in the Nth round according to Example 1 of the present application;

[0022] Figure 9 This is a schematic diagram of the framework of the deep learning model according to Example 1 of the present application;

[0023] Figure 10 2 is a schematic diagram of a device for reducing the impact of rain attenuation on a satellite gateway communication link according to Example 2 of the present application;

[0024] Figure 11 3 is a schematic diagram of a device for reducing the impact of rain attenuation on a satellite gateway communication link according to Example 3 of the present application. DETAILED DESCRIPTION

[0025] 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 part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present disclosure.

[0026] 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 numbers used in this way can be interchanged 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 clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Example 1

[0028] According to this embodiment, a method embodiment of a method for reducing the impact of rain attenuation on a satellite gateway communication link 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.

[0029] The method embodiment provided in this embodiment can be executed in a server or similar computing device. Figure 1 The hardware structure block diagram of a computing device for implementing a method for reducing the influence of rain attenuation on a satellite gateway communication link is shown. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a microprocessor MCU or a programmable logic device FPGA, etc.), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, transmission device, and 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. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0030] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, 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 components of the computing device. As described 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).

[0031] The memory can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the method for reducing the effects of rain fade on satellite gateway communication links in the embodiments of the present disclosure. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the aforementioned method for reducing the effects of rain fade on satellite gateway communication links. The memory can include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory can further include memory remotely located from the processor, which can be connected to the computing device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] The transmission device is used to receive or send data via a network. Specific examples of the aforementioned network may include a wireless network provided by a communications provider of the computing device. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computing device.

[0034] It should be noted that, in some optional embodiments, the above Figure 1 The computing device 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 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing device described above.

[0035] Figure 2 Schematic diagram of a system for reducing the impact of rain attenuation on satellite gateway communication links according to this embodiment. Figure 2 As shown, the system includes a satellite 10 and a gateway 20. Satellite 10 transmits each communication message to gateway 20 on the ground. Each time gateway 20 sends uplink data to the satellite, it adjusts the signal's transmit power and frequency based on the currently received message and previously communicated messages before sending the corresponding message back to satellite 10. Gateway 20 is equipped with a control center that analyzes the currently received message and previously communicated messages and adjusts the signal's transmit power and frequency based on the analysis results.

[0036] It should be noted that the control center in the gateway 20 may be adapted to the hardware structure described above.

[0037] Under the above operating environment, according to the first aspect of this embodiment, a method for reducing the influence of rain attenuation on the communication link of a satellite gateway is provided. The method comprises: Figure 2 The control center in the gateway station 20 shown in FIG is implemented. Figure 3 A schematic diagram showing the process of the method is shown in FIG. Figure 3 As shown, the method includes:

[0038] S302: Before the gateway sends the Nth round of signals to the satellite, determine gateway signal information, satellite feedback signal information, gateway location information, and satellite location information for rounds Nk to N-1; wherein the gateway signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in round Ni, and the receive power and receive frequency of the gateway signal received by the satellite in round Ni; the satellite feedback signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the satellite to the gateway in round Ni, and the receive power and receive frequency of the satellite signal received by the gateway in round Ni; i = 1 to k;

[0039] S304: Constructing a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Nk-th round to the N-1-th round; wherein the signal feature series is composed of signal features from the Nk-th round to the N-1-th round;

[0040] S306: Analyze the signal feature series using a pre-trained deep learning model to determine a power compensation value and a frequency compensation value of the signal transmitted by the gateway to the satellite in the Nth round; and

[0041] S308: Determine, based on the power compensation value and the frequency compensation value, the transmission power and the transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round.

[0042] In the embodiment of the present invention, a single round of communication is defined as a complete two-way signal exchange process between the gateway and the satellite: the gateway first sends a signal containing a message to the satellite via an uplink. After the satellite receives and processes the signal from the gateway, it returns a signal containing the corresponding message to the gateway via a downlink. Figure 4 In addition, a database is pre-set inside the gateway station for storing all messages generated by the two-way signal interaction process between the gateway station and the satellite.

[0043] Before gateway 20 transmits its Nth round of signals to satellite 10, a control center pre-installed within gateway 20 determines gateway signal information, satellite feedback signal information, gateway location information, and satellite location information for rounds Nk through N-1 (corresponding to step S302). Specifically, the control center first retrieves all messages generated between gateway 20 and satellite 10 during rounds Nk through N-1 from a database, parses the messages, and then extracts the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information for rounds Nk through N-1 from the messages. The value of k can be customized based on the actual application scenario. By determining the signal parameters (transmit / receive power, frequency) and location information of the communication link itself, real-time self-extraction and predictive perception of rain attenuation characteristics are achieved, providing a forward-looking basis for subsequent compensation strategies.

[0044] Taking N=10 and k=5 as an example, before gateway 20 sends the 10th round of signals to satellite 10, the control center needs to determine the gateway signal information, satellite feedback signal information, gateway position information, and satellite position information for rounds 5 to 9 (i.e., rounds 5, 6, 7, 8, and 9, for a total of 5 rounds). The gateway signal information for each of rounds 5 to 9 includes the transmit power and transmit frequency of the signal transmitted by gateway 20 to satellite 10 in the current round, and the receive power and receive frequency of the gateway signal received by satellite 10 in the current round. The satellite feedback signal information for each of rounds 5 to 9 includes the transmit power and transmit frequency of the signal transmitted by satellite 10 to gateway 20 in the current round, and the receive power and receive frequency of the satellite signal received by gateway 20 in the current round. Taking the gateway signal information and satellite feedback signal information of the ninth round as an example, the gateway signal information of this round includes the transmit power and frequency of the signal transmitted by gateway 20 to satellite 10 in the ninth round, and the receive power and frequency of the gateway signal received by satellite 10 in the ninth round. The satellite feedback signal information of this round includes the transmit power and frequency of the signal transmitted by satellite 10 to gateway 20 in the ninth round, and the receive power and frequency of the satellite signal received by gateway 20 in the ninth round.

[0045] The control center within gateway 20 then constructs a signal feature set based on gateway signal information from rounds Nk through N-1, satellite feedback signal information, gateway location information, and satellite location information (corresponding to step S304). This approach eliminates the need to wait for ground-based meteorological data (delay ≥ 1 minute). Instead, it directly extracts spatiotemporally coupled signal features (end-to-end delay < 10ms) that include rain fade effects from the gateway-satellite interaction signal and location information. This establishes an autonomous decision-making system that is independent of external data (e.g., meteorological data), laying the foundation for real-time, self-triggering of rain fade responses.

[0046] Next, the control center within gateway 20 uses a pre-trained deep learning model to analyze the signal feature series and determine the power and frequency compensation values ​​for the signal transmitted by the gateway to satellite 10 in the Nth round (corresponding to step S306). Specifically, a deep learning model can be pre-trained with the goal of accurately predicting the power and frequency compensation values ​​for the signal transmitted by the gateway to the satellite in the current round based on the signal features of multiple historical rounds. In other words, the deep learning model is required to learn the effects of rain attenuation on the power and frequency of the bidirectional communication signals between the gateway and the satellite from the signal features of multiple historical rounds and to generate corresponding power and frequency compensation values ​​based on these effects. The trained deep learning model is then deployed in the control center, allowing the control center to use it to analyze the signal feature series and determine the power and frequency compensation values ​​for the signal transmitted by the gateway to the satellite in the Nth round. In this way, the control center achieves a closed-loop link from feature input to compensation decision-making, providing precise adjustment instructions for subsequent dual power and frequency adjustments.

[0047] Finally, the control center inside the gateway station 20 determines the transmission power and transmission frequency of the signal transmitted by the gateway station 20 to the satellite 10 in the Nth round based on the power compensation value and the frequency compensation value (corresponding to step S308). Specifically, the control center determines the transmission power and transmission frequency of the signal transmitted by the gateway station 20 to the satellite 10 in the Nth round based on the power compensation value ΔP output by the deep learning model and the nominal transmission power P of the current round. nom (For example, the typical value of Ka band is 35dBW), through the formula P tx =P nom +ΔP calculates the target transmit power. The control center uses the frequency compensation value Δf output by the deep learning model and combines it with the channel center frequency f center (such as 28GHz), through the formula f tx =f center +Δf determines the target transmission frequency. In this way, the control center implements a complete closed loop from compensation value calculation to hardware parameter determination, ensuring that in the Nth round of communication, the gateway 20 sends signals to the satellite 10 at the optimal transmission power and frequency, thereby dynamically compensating for link rain attenuation and maintaining the signal in a suitable working state.

[0048] As mentioned in the background, existing rain fade mitigation solutions rely heavily on ground-based meteorological stations or satellite-borne rainfall monitoring equipment to obtain real-time rainfall rate data, resulting in a lag in response speed. Furthermore, existing solutions employ a fixed-frequency operating mode and only offer power compensation mechanisms, without the ability to dynamically adjust signal frequency, limiting their effectiveness.

[0049] In view of this, before the gateway sends the Nth round of signals to the satellite, the control center within the gateway determines the gateway signal information, satellite feedback signal information, gateway location information, and satellite position information for rounds Nk through N-1. By determining the various signal parameters (transmit / receive power, frequency) and position information of the communication link itself, this achieves real-time self-extraction and predictive perception of rain attenuation characteristics, providing a forward-looking basis for subsequent compensation strategies. The control center then constructs a signal feature set based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite position information for rounds Nk through N-1. Without waiting for ground meteorological station data (delay ≥ 1 minute), the control center directly extracts the spatiotemporally coupled attenuation characteristics (end-to-end delay < 10ms) from the gateway-satellite interaction signal and position information. This establishes an autonomous decision-making system that is independent of external data and lays the foundation for real-time self-triggering of rain attenuation response. Secondly, the control center uses a pre-trained deep learning model to analyze the signal feature series and determine the power and frequency compensation values ​​for the signal transmitted by the gateway to the satellite in the Nth round. This model simultaneously achieves power-frequency coordinated prediction through dual-objective optimization, providing precise adjustment instructions for subsequent dual power and frequency adjustments. Finally, based on the power and frequency compensation values, the control center determines the transmit power and frequency of the signal transmitted by the gateway to the satellite in the Nth round. This ensures that during the Nth round of communication, the gateway transmits signals to the satellite at the optimal transmit power and frequency, thereby dynamically compensating for rain attenuation in the link and maintaining the signal in a suitable operating state. This addresses the technical issues of existing satellite gateway anti-rain attenuation solutions, which rely excessively on meteorological data and only adjust signal power, resulting in lags in response speed and limitations in compensation effectiveness.

[0050] Optionally, the gateway signal information of the Ni-th round is determined by the following steps: obtaining a first message sent by the satellite to the gateway in the Ni-th round; wherein the first message includes a first message ID of the first message, a second message ID of a second message received by the satellite from the gateway in the Ni-th round, a receiving power and a receiving frequency of the gateway signal received by the satellite in the Ni-th round, a transmitting power and a transmitting frequency of the signal transmitted by the satellite to the gateway in the Ni-th round, satellite position information of the satellite, and a data area; obtaining the second message ID and the receiving power and receiving frequency of the gateway signal received by the satellite in the Ni-th round from the first message. rate; querying a preset database for a second message received by the satellite from the gateway in the Ni-th round according to the second message ID; and obtaining, from the second message, the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in the Ni-th round; wherein the second message includes the second message ID of the second message, the third message ID of the third message received by the gateway from the satellite in the N-(i+1)-th round, the receive power and receive frequency of the satellite signal received by the gateway in the N-(i+1)-th round, the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in the Ni-th round, the satellite position information of the gateway, and a data area.

[0051] In the embodiment of the present invention, the format of the message sent by the gateway to the satellite and the format of the message sent by the satellite to the gateway are defined. Specifically, the message sent by the gateway to the satellite mainly includes four parts. The first part is the message ID of the message sent by the gateway to the satellite in the current round. The second part is the relevant information of the message received by the gateway from the satellite in the previous round (i.e., the round before the current round) (including the message ID, the receiving power and receiving frequency of the received satellite signal). The third part is the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the current round, as well as the location information of the gateway in the current round. The fourth part is the data area, such as Figure 5 and Figure 7 The message sent by the satellite to the gateway station mainly includes four parts. The first part is the message ID of the message sent by the satellite to the gateway station in the current round. The second part is the relevant information of the message received by the satellite from the satellite in the current round (including the message ID, the receiving power and receiving frequency of the received gateway signal). The third part is the transmission power and transmission frequency of the signal transmitted by the satellite to the gateway station in the current round, as well as the satellite position information in the current round. The fourth part is the data area, as shown in Figure 1. Figure 6 and Figure 8 shown.

[0052] Therefore, in the process of determining the signal information of the gateway station in the Ni-th round, the control center can first obtain the first message sent by the satellite to the gateway station in the Ni-th round. Figure 6 As shown, the first message includes a first message ID of the first message, a second message ID of a second message received by the satellite from the gateway in the N-1th round, the receiving power and receiving frequency of the gateway signal received by the satellite in the N-1th round, the transmitting power and transmitting frequency of the signal transmitted by the satellite to the gateway in the N-1th round, satellite position information of the satellite, and a data area.

[0053] Then, the control center can obtain the second message ID (corresponding to Figure 6 The "message ID of the message received in the N-1th round" in the "N-1th round" and the receiving power and receiving frequency of the gateway station signal received by the satellite in the Ni-th round. In addition, according to the second message ID, a preset database is searched for the second message received by the satellite from the gateway station in the N-1th round, and the transmission power and transmission frequency of the signal transmitted by the gateway station to the satellite in the N-1th round are obtained from the second message. Figure 5 As shown, the second message includes the second message ID of the second message (corresponding to Figure 5 The "message ID of the message sent in the N-1th round"), the third message ID of the third message received by the gateway from the satellite in the N-2th round (corresponding to Figure 5 the “message ID of the message received in the N-2th round”), the receiving power and receiving frequency of the satellite signal received by the gateway in the N-2th round, the transmitting power and transmitting frequency of the signal transmitted by the gateway to the satellite in the N-1th round, the satellite position information of the gateway, and the data area.

[0054] In this way, the gateway signal information of the Nith round can be quickly and accurately determined from the messages of the historical rounds.

[0055] Optionally, the satellite feedback signal information of the Ni-th round is determined by the following steps: obtaining the transmission power and transmission frequency of the signal transmitted by the satellite to the gateway station in the Ni-th round from the first message; and analyzing the signal transmitted by the satellite to the gateway station in the Ni-th round to determine the receiving power and receiving frequency of the satellite signal received by the gateway station in the Ni-th round.

[0056] In the embodiment of the present invention, the control center continues to use the above example of i=1 in the process of determining the satellite feedback signal information of the Ni-th round, referring to Figure 6As shown, the control center can directly obtain the transmission power and transmission frequency of the signal transmitted by the satellite to the gateway station in the N-1th round from the first message. The control center needs to analyze the signal transmitted by the satellite to the gateway station in the N-1th round to determine the receiving power and receiving frequency of the satellite signal received by the gateway station in the N-1th round. In addition, the control center needs to write the analyzed receiving power and receiving frequency of the satellite signal received in the N-1th round into the second part of the message before the gateway station sends a message to the satellite in the Nth round, refer to Figure 7 shown.

[0057] In this way, a part of the satellite feedback signal information of the Nith round can be quickly and accurately obtained from the messages of the historical rounds, and the other part of the information can be determined by analyzing the signal.

[0058] Optionally, the gateway location information and satellite location information of the Nith round are determined by the following steps: obtaining the satellite location information of the Nith round from the first message; and obtaining the gateway location information of the Nith round from the second message.

[0059] In the embodiment of the present invention, the above example of i=1 is continued, referring to Figure 6 As shown, since the fourth part of the first message contains the satellite position information of the satellite in the N-1th round of communication, the satellite position information of the N-1th round can be directly obtained from the first message. Figure 5 As shown, since the fourth part of the second message records the gateway location information in the N-1th round of communication, the gateway location information in the N-1th round can be directly obtained from the second message.

[0060] Optionally, the operation of constructing a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Nk-th round to the N-1-th round includes: performing independent feature extraction on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Ni-th round and then splicing them to obtain signal features from the Ni-th round; i = 1 to k; and constructing the signal feature series based on the signal features from the Nk-th round to the N-1-th round.

[0061] In this embodiment of the present invention, continuing with the example of N = 10 and k = 5, after the control center determines the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information for rounds 5 through 9, it can extract features from each round of gateway signal information, satellite feedback signal information, gateway location information, and satellite location information, and concatenate the extracted features. This concatenated feature serves as the signal feature for the corresponding round, integrating both temporal and spatial domain features. The control center then constructs a spatiotemporally coupled signal feature set based on the signal features from rounds 5 through 9, providing a data-driven decision-making basis for subsequent power and frequency dual regulation.

[0062] Optionally, the pre-trained deep learning model includes an input layer, a bidirectional temporal coding layer, a spatial attention layer, and a fully connected prediction layer; wherein the fully connected prediction layer includes one hidden unit and two output units; and the operation of using the pre-trained deep learning model to analyze the signal feature series and determine the power compensation value and frequency compensation value of the signal transmitted by the gateway to the satellite in the Nth round includes: sending the signal feature series to the bidirectional temporal coding layer through the input layer, extracting temporal features of the signal feature series through the bidirectional temporal coding layer; extracting the temporal features of the signal feature series output by the bidirectional temporal coding layer; and The sequence feature vector is spliced ​​with the gateway station position feature and satellite position feature of the N-1th round to form a space-time joint feature vector; the space-time joint feature vector is input into the spatial attention layer and further processed by the spatial attention layer; and the enhanced feature vector output by the spatial attention layer is transmitted to the fully connected prediction layer. After nonlinear transformation is performed by the hidden unit, one of the two output units outputs the power compensation value of the signal transmitted by the gateway station to the satellite in the Nth round, and the other of the two output units outputs the frequency compensation value of the signal transmitted by the gateway station to the satellite in the Nth round.

[0063] In the embodiment of the present invention, referring to Figure 9 As shown in Figure 1, the pre-trained deep learning model includes an input layer, a bidirectional temporal coding layer, a spatial attention layer, and a fully connected prediction layer. The fully connected prediction layer includes one hidden unit and two output units. The signal feature series X = [x N-k ,x N-(k+1) ,...,x N-1 ], where x N-k is the signal feature of the Nkth round, x N-(k+1) is the signal feature of the N-(k+1)th round, x N-1 Signal characteristics of the N-1th round. x i ∈R D, where i = 1 to k, and D = feature dimension. In this embodiment, D = 3, i.e., power, frequency, and position coordinates. Continuing with the example of N = 10 and k = 5, the signal feature series X = [x5, x6, ..., x9].

[0064] The signal feature series X = [x N-k ,x N-(k+1) ,...,x N-1 ] is passed to the bidirectional temporal coding layer through the input layer, and the signal feature series is encoded into a temporal feature vector representation of temporal context awareness through the bidirectional temporal coding layer. Specifically, the forward temporal dependency of the i-th signal feature is captured by the bidirectional temporal coding layer (BiLSTM). and backward timing dependencies And concatenate the forward timing dependency and the backward timing dependency to obtain the timing feature of each signal feature Where H is the dimension of the unidirectional hidden layer, i = 1 ~ k. Then, the time series features of all signal features in the signal feature series are combined to obtain the time series feature vector H = [h N-k ,h N-(k+1) ,...,h N-1 ].

[0065] Then, the temporal feature vector H output by the bidirectional temporal coding layer is concatenated with the gateway location features and satellite location features of the N-1th round to form a space-time joint feature vector V = [h N-k ,h N-(k+1) ,...,h N-1 ,f 1,N-k ,f 1,N-(k+1) ,...,f 1,N-1 ,f 2,N-k ,f 2,N-(k+1) ,...,f 2,N-1 ]. Among them, f 1,N-k is the location feature of the gateway in the Nkth round, f 1,N-(k+1) is the location feature of the gateway in the N-(k+1)th round, f 1,N-1 is the location feature of the gateway in round N-1, f 2,N-k is the satellite position feature of the Nkth round, f 2,N-(k+1) is the satellite position feature of the N-(k+1)th round, f 2,N-1 is the satellite position feature of the N-1th round. After that, the spatial-temporal joint feature vector V is input into the spatial attention layer, and further processed by the spatial attention layer to output the enhanced feature vector V′, V′=LayerNorm(V+A), A=SV∈R d , d is the dimension of attention head, S is the similarity matrix, Q=WQ V, K = W K V, V = W V V, W Q 、W K and W V The Query (Q), Key (K), and Value (V) matrices are generated by linear transformation of the spatial-temporal joint feature vector V.

[0066] Finally, the enhanced feature vector V′ output by the spatial attention layer is transmitted to the fully connected prediction layer. After nonlinear transformation by the hidden unit, one of the two output units outputs the power compensation value ΔP of the signal transmitted by the gateway to the satellite in the Nth round, and the other outputs the frequency compensation value Δf of the signal transmitted by the gateway to the satellite in the Nth round.

[0067] Optionally, the operation of determining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round based on the power compensation value and the frequency compensation value includes: obtaining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-1th round from the second message; and determining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round based on the power compensation value and the frequency compensation value based on the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-1th round.

[0068] In the embodiment of the present invention, the control center determines the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round according to the power compensation value and the frequency compensation value, and can not only center (such as 28GHz) and nominal transmission power P nom (such as the typical value of Ka band 35dBW) is compensated on the basis of the second message, and the transmission power P of the signal transmitted by the gateway to the satellite in the N-1th round can also be obtained from the second message. N-1 and the transmitting frequency f N-1 , then by formula P N =P N-1 +ΔP calculates the transmission power of the signal transmitted by the gateway station to the satellite in the Nth round, and uses the formula f N =f N-1 +Δf is the transmission frequency of the signal sent by the gateway to the satellite in round N. This method enables dynamic adjustment of the transmission parameters (power + frequency) in milliseconds, ensuring that the signal maintains optimal operating conditions under complex channel conditions such as rain fading.

[0069] In addition, reference Figure 1As shown, according to a 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.

[0070] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0071] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially 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.

[0072] Example 2

[0073] Figure 10 The apparatus for reducing the influence of rain attenuation on the communication link of a satellite gateway station according to this embodiment is shown, and the apparatus corresponds to the method according to embodiment 1. Figure 10As shown, the apparatus includes: an information determination module 1010, configured to determine gateway signal information, satellite feedback signal information, gateway location information, and satellite location information for rounds Nk to N-1 before the gateway sends the Nth round of signals to the satellite; wherein the gateway signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in round Ni, and the receive power and receive frequency of the gateway signal received by the satellite in round Ni; the satellite feedback signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the satellite to the gateway in round Ni, and the receive power and receive frequency of the satellite signal received by the gateway in round Ni; i=1~ k; a feature determination module 1020, configured to construct a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Nk-th round to the N-1-th round; wherein the signal feature series is composed of the signal features from the Nk-th round to the N-1-th round; a compensation value determination module 1030, configured to analyze the signal feature series using a pre-trained deep learning model to determine a power compensation value and a frequency compensation value for the signal transmitted by the gateway to the satellite in the N-th round; and a power and frequency determination module 1040, configured to determine, based on the power compensation value and the frequency compensation value, the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-th round.

[0074] Optionally, the gateway signal information of the Ni-th round is determined by the following steps: obtaining a first message sent by the satellite to the gateway in the Ni-th round; wherein the first message includes a first message ID of the first message, a second message ID of a second message received by the satellite from the gateway in the Ni-th round, a receiving power and a receiving frequency of the gateway signal received by the satellite in the Ni-th round, a transmitting power and a transmitting frequency of the signal transmitted by the satellite to the gateway in the Ni-th round, satellite position information of the satellite, and a data area; obtaining the second message ID and the receiving power and receiving frequency of the gateway signal received by the satellite in the Ni-th round from the first message. rate; querying a preset database for a second message received by the satellite from the gateway in the Ni-th round according to the second message ID; and obtaining, from the second message, the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in the Ni-th round; wherein the second message includes the second message ID of the second message, the third message ID of the third message received by the gateway from the satellite in the N-(i+1)-th round, the receive power and receive frequency of the satellite signal received by the gateway in the N-(i+1)-th round, the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in the Ni-th round, the satellite position information of the gateway, and a data area.

[0075] Optionally, the satellite feedback signal information of the Ni-th round is determined by the following steps: obtaining the transmission power and transmission frequency of the signal transmitted by the satellite to the gateway station in the Ni-th round from the first message; and analyzing the signal transmitted by the satellite to the gateway station in the Ni-th round to determine the receiving power and receiving frequency of the satellite signal received by the gateway station in the Ni-th round.

[0076] Optionally, the gateway location information and satellite location information of the Nith round are determined by the following steps: obtaining the satellite location information of the Nith round from the first message; and obtaining the gateway location information of the Nith round from the second message.

[0077] Optionally, the operation of constructing a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Nk-th round to the N-1-th round includes: performing independent feature extraction on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Ni-th round and then splicing them to obtain signal features from the Ni-th round; i = 1 to k; and constructing the signal feature series based on the signal features from the Nk-th round to the N-1-th round.

[0078] Optionally, the pre-trained deep learning model includes an input layer, a bidirectional temporal coding layer, a spatial attention layer, and a fully connected prediction layer; wherein the fully connected prediction layer includes one hidden unit and two output units; and the operation of using the pre-trained deep learning model to analyze the signal feature series and determine the power compensation value and frequency compensation value of the signal transmitted by the gateway to the satellite in the Nth round includes: sending the signal feature series to the bidirectional temporal coding layer through the input layer, extracting temporal features of the signal feature series through the bidirectional temporal coding layer; extracting the temporal features of the signal feature series output by the bidirectional temporal coding layer; and The sequence feature vector is spliced ​​with the gateway station position feature and satellite position feature of the N-1th round to form a space-time joint feature vector; the space-time joint feature vector is input into the spatial attention layer and further processed by the spatial attention layer; and the enhanced feature vector output by the spatial attention layer is transmitted to the fully connected prediction layer. After nonlinear transformation is performed by the hidden unit, one of the two output units outputs the power compensation value of the signal transmitted by the gateway station to the satellite in the Nth round, and the other of the two output units outputs the frequency compensation value of the signal transmitted by the gateway station to the satellite in the Nth round.

[0079] Optionally, the operation of determining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round based on the power compensation value and the frequency compensation value includes: obtaining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-1th round from the second message; and determining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round based on the power compensation value and the frequency compensation value based on the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-1th round.

[0080] Therefore, according to this embodiment, before the gateway sends the Nth round of signals to the satellite, the information determination module 1010 determines the gateway signal information, satellite feedback signal information, gateway location information, and satellite position information for rounds Nk through N-1. By determining the various signal parameters (transmit / receive power, frequency) and position information of the communication link itself, real-time self-extraction and predictive perception of rain fade characteristics are achieved, providing a forward-looking basis for subsequent compensation strategies. Then, the feature determination module 1020 constructs a signal feature set based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite position information for rounds Nk through N-1. This eliminates the need to wait for ground meteorological station data (delay ≥ 1 minute) and instead directly extracts spatiotemporally coupled attenuation characteristics (end-to-end delay < 10ms) from the gateway-satellite interaction signal and position information. This establishes an autonomous decision-making system that is independent of external data and lays the foundation for real-time self-triggering of rain fade response. Secondly, compensation value determination module 1030 uses a pre-trained deep learning model to analyze the signal feature series and determine the power and frequency compensation values ​​for the signal transmitted by the gateway to the satellite in the Nth round. This model simultaneously achieves power-frequency coordinated prediction through dual-objective optimization, providing precise adjustment instructions for subsequent dual power and frequency adjustments. Finally, power and frequency determination module 1040 determines the transmit power and frequency of the signal transmitted by the gateway to the satellite in the Nth round based on the power and frequency compensation values. This ensures that in the Nth round of communication, the gateway transmits signals to the satellite at the optimal transmit power and frequency, thereby dynamically compensating for rain attenuation in the link and maintaining the signal in a suitable operating state. This addresses the technical issues of existing satellite gateway anti-rain attenuation solutions, which rely excessively on meteorological data and only adjust signal power, resulting in lags in response speed and limitations in compensation effectiveness.

[0081] Example 3

[0082] Figure 11 The apparatus for reducing the influence of rain attenuation on the communication link of a satellite gateway station according to this embodiment is shown, and the apparatus corresponds to the method according to embodiment 1. Figure 11As shown, the device includes: a processor 1110; and a memory 1120, connected to the processor 1110, and configured to provide the processor 1110 with instructions for processing the following processing steps: before the gateway station sends the Nth round of signals to the satellite, determining the gateway station signal information, satellite feedback signal information, gateway station position information and satellite position information from the Nkth round to the N-1th round; wherein the gateway station signal information of the Nith round includes the transmission power and transmission frequency of the signal transmitted by the gateway station to the satellite in the Nith round, and the receiving power and receiving frequency of the gateway station signal received by the satellite in the Nith round; the satellite feedback signal information of the Nith round includes the transmission power and transmission frequency of the signal transmitted by the satellite to the gateway station in the Nith round. the receiving power and frequency of the satellite signal received by the gateway in the N-th round; i=1-k; constructing a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information and satellite location information from the Nk-th round to the N-1-th round; wherein the signal feature series is composed of the signal features from the Nk-th round to the N-1-th round; analyzing the signal feature series using a pre-trained deep learning model to determine a power compensation value and a frequency compensation value of the signal transmitted by the gateway to the satellite in the N-th round; and determining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-th round based on the power compensation value and the frequency compensation value.

[0083] Optionally, the gateway signal information of the Ni-th round is determined by the following steps: obtaining a first message sent by the satellite to the gateway in the Ni-th round; wherein the first message includes a first message ID of the first message, a second message ID of a second message received by the satellite from the gateway in the Ni-th round, a receiving power and a receiving frequency of the gateway signal received by the satellite in the Ni-th round, a transmitting power and a transmitting frequency of the signal transmitted by the satellite to the gateway in the Ni-th round, satellite position information of the satellite, and a data area; obtaining the second message ID and the receiving power and receiving frequency of the gateway signal received by the satellite in the Ni-th round from the first message. rate; querying a preset database for a second message received by the satellite from the gateway in the Ni-th round according to the second message ID; and obtaining, from the second message, the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in the Ni-th round; wherein the second message includes the second message ID of the second message, the third message ID of the third message received by the gateway from the satellite in the N-(i+1)-th round, the receive power and receive frequency of the satellite signal received by the gateway in the N-(i+1)-th round, the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in the Ni-th round, the satellite position information of the gateway, and a data area.

[0084] Optionally, the satellite feedback signal information of the Ni-th round is determined by the following steps: obtaining the transmission power and transmission frequency of the signal transmitted by the satellite to the gateway station in the Ni-th round from the first message; and analyzing the signal transmitted by the satellite to the gateway station in the Ni-th round to determine the receiving power and receiving frequency of the satellite signal received by the gateway station in the Ni-th round.

[0085] Optionally, the gateway location information and satellite location information of the Nith round are determined by the following steps: obtaining the satellite location information of the Nith round from the first message; and obtaining the gateway location information of the Nith round from the second message.

[0086] Optionally, the operation of constructing a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Nk-th round to the N-1-th round includes: performing independent feature extraction on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Ni-th round and then splicing them to obtain signal features from the Ni-th round; i = 1 to k; and constructing the signal feature series based on the signal features from the Nk-th round to the N-1-th round.

[0087] Optionally, the pre-trained deep learning model includes an input layer, a bidirectional temporal coding layer, a spatial attention layer, and a fully connected prediction layer; wherein the fully connected prediction layer includes one hidden unit and two output units; and the operation of using the pre-trained deep learning model to analyze the signal feature series and determine the power compensation value and frequency compensation value of the signal transmitted by the gateway to the satellite in the Nth round includes: sending the signal feature series to the bidirectional temporal coding layer through the input layer, extracting temporal features of the signal feature series through the bidirectional temporal coding layer; extracting the temporal features of the signal feature series output by the bidirectional temporal coding layer; and The sequence feature vector is spliced ​​with the gateway station position feature and satellite position feature of the N-1th round to form a space-time joint feature vector; the space-time joint feature vector is input into the spatial attention layer and further processed by the spatial attention layer; and the enhanced feature vector output by the spatial attention layer is transmitted to the fully connected prediction layer. After nonlinear transformation is performed by the hidden unit, one of the two output units outputs the power compensation value of the signal transmitted by the gateway station to the satellite in the Nth round, and the other of the two output units outputs the frequency compensation value of the signal transmitted by the gateway station to the satellite in the Nth round.

[0088] Optionally, the operation of determining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round based on the power compensation value and the frequency compensation value includes: obtaining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-1th round from the second message; and determining the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round based on the power compensation value and the frequency compensation value based on the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-1th round.

[0089] Therefore, according to this embodiment, before the gateway sends the Nth round of signals to the satellite, it first determines the gateway signal information, satellite feedback signal information, gateway location information, and satellite position information for rounds Nk through N-1. By determining the various signal parameters (transmit / receive power, frequency) and position information of the communication link itself, real-time self-extraction and predictive perception of rain fade characteristics are achieved, providing a forward-looking basis for subsequent compensation strategies. Then, based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite position information for rounds Nk through N-1, a signal feature set is constructed. Instead of waiting for ground meteorological station data (delay ≥ 1 minute), the spatiotemporally coupled attenuation characteristics (end-to-end delay < 10ms) are directly extracted from the gateway-satellite interaction signal and position information. This establishes an autonomous decision-making system that is independent of external data and lays the foundation for real-time self-triggering of rain fade response. Secondly, a pre-trained deep learning model is used to analyze the signal feature series and determine the power and frequency compensation values ​​for the signal transmitted by the gateway to the satellite in the Nth round. This model simultaneously achieves power-frequency coordinated prediction through dual-objective optimization, providing precise adjustment instructions for subsequent dual power and frequency adjustments. Finally, based on the power and frequency compensation values, the gateway determines the transmit power and frequency of the signal transmitted to the satellite in the Nth round. This ensures that in the Nth round of communication, the gateway transmits signals to the satellite at the optimal transmit power and frequency, thereby dynamically compensating for rain attenuation in the link and maintaining the signal in a suitable operating state. This addresses the technical issues of existing satellite gateway anti-rain attenuation solutions, which rely excessively on meteorological data and only adjust signal power, resulting in lags in response speed and limitations in compensation effectiveness.

[0090] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0091] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] 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. In actual implementation, there may be other division methods, such as 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.

[0093] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0094] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0095] 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, 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 and includes several instructions for enabling 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, magnetic disk or optical disk, etc. Various media that can store program codes.

[0096] 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 principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for reducing the impact of rain attenuation on a satellite gateway communication link, characterized in that: include: Before the gateway sends the Nth round of signals to the satellite, determining gateway signal information, satellite feedback signal information, gateway position information, and satellite position information for rounds Nk to N-1; wherein the gateway signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in round Ni, and the receive power and receive frequency of the gateway signal received by the satellite in round Ni; the satellite feedback signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the satellite to the gateway in round Ni, and the receive power and receive frequency of the satellite signal received by the gateway in round Ni; i = 1 to k; Constructing a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Nk-th to N-1-th rounds; wherein the signal feature series is composed of the signal features from the Nk-th to N-1-th rounds; Analyzing the signal feature series using a pre-trained deep learning model to determine a power compensation value and a frequency compensation value of a signal transmitted by the gateway to the satellite in the Nth round; and The transmission power and frequency of the signal transmitted by the gateway to the satellite in the Nth round are determined according to the power compensation value and the frequency compensation value.

2. The method according to claim 1, characterized in that Determine the gateway station signal information for round Ni by following the steps below: Obtaining a first message sent by the satellite to the gateway in round N; The first message includes a first message ID of the first message, a second message ID of a second message received by the satellite from the gateway in round N, a receiving power and a receiving frequency of a gateway signal received by the satellite in round N, a transmitting power and a transmitting frequency of a signal transmitted by the satellite to the gateway in round N, satellite position information of the satellite, and a data area; Obtaining the second message ID and the receiving power and receiving frequency of the gateway signal received by the satellite in the Nith round from the first message; querying a preset database according to the second message ID for the second message received by the satellite from the gateway in the Nith round; as well as Obtaining, from the second message, the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-th round; wherein the second message includes a second message ID of the second message, a third message ID of the third message received by the gateway from the satellite in the N-(i+1)-th round, the reception power and reception frequency of the satellite signal received by the gateway in the N-(i+1)-th round, the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-th round, satellite position information of the gateway, and a data area.

3. The method according to claim 2, characterized in that Determine the satellite feedback signal information for round Ni by following the steps below: Obtaining, from the first message, the transmission power and frequency of the signal transmitted by the satellite to the gateway in the Nith round; and Analyze the signal transmitted by the satellite to the gateway in the Ni-th round to determine the receiving power and receiving frequency of the satellite signal received by the gateway in the Ni-th round.

4. The method according to claim 2, characterized in that Determine the gateway location information and satellite location information for the Nith round by following the steps below: Obtaining satellite position information of the Ni-th round from the first message; and The gateway station location information of the Ni-th round is obtained from the second message.

5. The method according to claim 1, wherein The operation of constructing a signal feature series based on the gateway station signal information, satellite feedback signal information, gateway station position information and satellite position information from the Nk-th round to the N-1-th round includes: Extract independent features from the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information of the Ni-th round and then combine them to obtain the signal features of the Ni-th round; i = 1 to k; and The signal feature series is constructed based on the signal features from the Nk-th round to the N-1-th round.

6. The method according to claim 1, characterized in that The pre-trained deep learning model includes an input layer, a bidirectional temporal coding layer, a spatial attention layer, and a fully connected prediction layer; wherein the fully connected prediction layer includes one hidden unit and two output units; The operation of analyzing the signal feature series using a pre-trained deep learning model to determine the power compensation value and the frequency compensation value of the signal transmitted by the gateway to the satellite in the Ni-th round includes: Sending the signal feature series to the bidirectional temporal coding layer through the input layer, and performing temporal feature extraction on the signal feature series through the bidirectional temporal coding layer; The time series feature vector output by the bidirectional time series coding layer is concatenated with the gateway position feature and satellite position feature of the N-1th round to form a space-time joint feature vector; Inputting the joint spatial-temporal feature vector into the spatial attention layer for further processing by the spatial attention layer; and The enhanced feature vector output by the spatial attention layer is transmitted to the fully connected prediction layer. After nonlinear transformation is performed by the hidden unit, one of the two output units outputs a power compensation value of the signal transmitted by the gateway to the satellite in the Nth round, and the other of the two output units outputs a frequency compensation value of the signal transmitted by the gateway to the satellite in the Nth round.

7. The method according to claim 2, characterized in that The operation of determining, based on the power compensation value and the frequency compensation value, the transmission power and the transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round includes: Obtaining, from the second message, the transmission power and frequency of the signal transmitted by the gateway to the satellite in the N-1th round; and Based on the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the N-1th round, and according to the power compensation value and the frequency compensation value, determine the transmission power and transmission frequency of the signal transmitted by the gateway to the satellite in the Nth round.

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

9. A device for reducing the influence of rain attenuation on satellite gateway communication links, characterized in that: include: An information determination module is configured to determine gateway signal information, satellite feedback signal information, gateway location information, and satellite location information for rounds Nk to N-1 before the gateway sends the Nth round of signals to the satellite. The gateway signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in round Ni, and the receive power and receive frequency of the gateway signal received by the satellite in round Ni. The satellite feedback signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the satellite to the gateway in round Ni, and the receive power and receive frequency of the satellite signal received by the gateway in round Ni. i = 1 to k. a feature determination module, configured to construct a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Nk-th to N-1-th rounds; wherein the signal feature series is composed of the signal features from the Nk-th to N-1-th rounds; a compensation value determination module, configured to analyze the signal feature series using a pre-trained deep learning model to determine a power compensation value and a frequency compensation value of the signal transmitted by the gateway to the satellite in the Nth round; and The power and frequency determination module is configured to determine the transmission power and frequency of the signal transmitted by the gateway to the satellite in the Nth round according to the power compensation value and the frequency compensation value.

10. A device for reducing the influence of rain attenuation on satellite gateway communication links, 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: Before the gateway sends the Nth round of signals to the satellite, determining gateway signal information, satellite feedback signal information, gateway position information, and satellite position information for rounds Nk to N-1; wherein the gateway signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the gateway to the satellite in round Ni, and the receive power and receive frequency of the gateway signal received by the satellite in round Ni; the satellite feedback signal information for round Ni includes the transmit power and transmit frequency of the signal transmitted by the satellite to the gateway in round Ni, and the receive power and receive frequency of the satellite signal received by the gateway in round Ni; i = 1 to k; Constructing a signal feature series based on the gateway signal information, satellite feedback signal information, gateway location information, and satellite location information from the Nk-th to N-1-th rounds; wherein the signal feature series is composed of the signal features from the Nk-th to N-1-th rounds; Analyzing the signal feature series using a pre-trained deep learning model to determine a power compensation value and a frequency compensation value of a signal transmitted by the gateway to the satellite in the Nth round; and The transmission power and frequency of the signal transmitted by the gateway to the satellite in the Nth round are determined according to the power compensation value and the frequency compensation value.

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