Beam hopping interference processing method in multi-satellite scene and related equipment

Abstract

The invention provides a hopping beam interference processing method in a multi-satellite scene and related equipment, and relates to the technical field of wireless communication. The method comprises the steps that a multi-satellite-hopping-beam system model is established, the multi-satellite-hopping-beam system model comprises a plurality of satellites, each satellite carries a plurality of beams, and each beam serves a cell; determining a hopping beam pattern of each satellite under each time slot by taking maximization of system throughput as a target; for adjacent cells in each time slot hopping beam pattern, adjusting a partial frequency reuse scheme of the adjacent cells according to the service flow requirements of edge users of the adjacent cells so as to maximize the edge user satisfaction of the adjacent cells; and outputting a partial frequency reuse scheme of the adjacent cell under the condition of maximizing the edge user satisfaction of the adjacent cell. According to the method and the device, the same-frequency interference can be effectively reduced and the communication quality in a multi-satellite scene can be improved while the system throughput is ensured and the dynamic beam scheduling requirement is met.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless communication technology, and in particular to a beam-hopping interference processing method and related equipment in a multi-satellite scenario. Background Art

[0002] Beam-hopping technology uses time slicing to optimize satellite resources in the time domain. In a single time slot, rather than serving all cells within a satellite's coverage area, beams are selectively scheduled to serve only a subset of cells. In low-orbit multi-satellite scenarios, when each satellite serves a cell using its own beam-hopping pattern, two types of interference can occur: intra-satellite interference. When a single satellite serves a cell using its own beam-hopping pattern, inter-cluster interference (i.e., co-channel interference caused by adjacent cells being served simultaneously) can occur. Inter-satellite interference (i.e., when multiple satellites serve a cell, co-channel interference can occur, potentially resulting from adjacent cells being served simultaneously by different satellites. Furthermore, the same cell can be served by multiple satellites simultaneously, further exacerbating co-channel interference. This intra-satellite or inter-satellite co-channel interference can severely impact communication quality. Therefore, in multi-satellite scenarios, an efficient interference avoidance method is needed to reduce co-channel interference and improve communication quality.

[0003] Currently, there are two main interference avoidance methods in related technologies: multi-color frequency reuse (MFR) and fractional frequency reuse (FFR). MFR evenly divides the frequency band into non-overlapping sub-bands, allocating them to adjacent cells to avoid co-channel interference, but this significantly reduces system throughput. FFR divides the frequency band into center sub-bands (for center users, with a frequency reuse factor of 1) and edge sub-bands (for edge users, with a frequency reuse factor greater than 1). However, because the frequency band allocation for edge users is typically fixed at the initial deployment stage and lacks a dynamic scheduling mechanism, it cannot cope with the current complex and changing satellite communication environment.

[0004] Therefore, how to provide a more intelligent and efficient interference avoidance method that can effectively reduce co-channel interference and improve communication quality while ensuring system throughput and meeting dynamic scheduling beams is a technical problem that needs to be solved urgently.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0006] The present disclosure provides a beam-hopping interference processing method and related equipment in a multi-satellite scenario, which at least to a certain extent overcomes the technical problem that the interference avoidance methods provided in the related art are difficult to take into account the suppression of intra-satellite / inter-satellite co-frequency interference, flexible beam scheduling, and maximization of system throughput.

[0007] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0008] According to one aspect of the present disclosure, a method for processing beam-hopping interference in a multi-satellite scenario is provided, including: establishing a multi-satellite beam-hopping system model, wherein the multi-satellite beam-hopping system model includes: multiple satellites, each satellite carrying multiple beams, and each beam serving one cell; determining the beam-hopping pattern of each satellite in each time slot with the goal of maximizing system throughput; for adjacent cells in each time slot beam-hopping pattern, adjusting the partial frequency reuse scheme of the adjacent cells according to the service traffic requirements of the edge users of the adjacent cells to maximize the edge user satisfaction of the adjacent cells, wherein the partial frequency reuse scheme is used to allocate frequency resources to the center users and edge users of the adjacent cells, and the edge user satisfaction is determined based on the frequency resources allocated to the edge users and the frequency resources required by the edge users; and outputting the partial frequency reuse scheme of the adjacent cells that maximizes the edge user satisfaction of the adjacent cells.

[0009] In some embodiments, after determining the beam hopping pattern of each satellite in each time slot with the goal of maximizing system throughput, the method further includes: for overlapping cells served by multiple satellites, determining a satellite with the highest signal-to-interference-plus-noise ratio among the multiple satellites as a serving satellite for the overlapping cell, and selecting a cell with the next highest signal-to-interference-plus-noise ratio for the remaining satellites among the multiple satellites except the serving satellite to serve.

[0010] In some embodiments, with the goal of maximizing system throughput, the beam hopping pattern of each satellite in each time slot is determined, including: establishing a first objective function with the goal of maximizing system throughput; converting the optimization problem of the first objective function into a first Markov decision process to obtain the beam hopping pattern of each satellite in each time slot under the maximized system throughput, wherein the reward function of the first Markov decision process is the first objective function, the state of the first Markov decision process is the cell environment state, and the execution action of the first Markov decision process is the beam hopping pattern selection action.

[0011] In some embodiments, the first objective function and the corresponding constraints are expressed as follows:

[0012]

[0013]

[0014] Where T represents the total number of time slots; t represents the time slot number; M represents the total number of satellites; m represents the satellite number; N represents the total number of cells that each satellite can cover; n represents the cell number; represents the throughput of satellite m serving cell n in time slot t, represents the channel capacity of satellite m covering cell n in time slot t, is the service demand of cell n covered by satellite m in time slot t;

[0015] Among them, the first constraint t∈T represents the number of cells served within the satellite coverage area in each time slot The number of effective beam payloads K carried by the satellite does not exceed the second constraint. t∈T represents the power allocated to all cells within the satellite coverage area in each time slot Not exceeding the total power P of the satellite tot ; M represents the satellite set; T represents the time slot set; θ m Represents the set of cells covered by satellite m.

[0016] In some embodiments, the state, action, and reward functions of the first Markov decision process are expressed as follows:

[0017]

[0018]

[0019]

[0020] Among them, s t represents the state of the first Markov decision process at time slot t; represents the environmental status information of cell n at time slot t; n represents the cell number; N represents the set of cells that can be covered by each satellite; m represents the satellite number; M represents the satellite set; θ m represents the set of cells covered by satellite m; a t represents the action performed by the first Markov decision process at time slot t; represents the beam hopping pattern selection; R t represents the reward value of the first Markov decision process at time slot t; represents the throughput of satellite m serving cell n in time slot t; the cumulative reward value Q(s t ,a t ) is updated as follows:

[0021]

[0022] Among them, s t+1 represents the state of the first Markov decision process at time slot t+1; a t+1 represents the execution action of a Markov decision process at time slot t+1; Rt+1 represents the reward value of the first Markov decision process at time slot t+1; γ represents the discount factor; α represents the learning rate.

[0023] In some embodiments, for adjacent cells in each time slot hopping beam pattern, the partial frequency reuse scheme of the adjacent cells is adjusted according to the service traffic demand of the edge users of the adjacent cells to maximize the edge user satisfaction of the adjacent cells, including: establishing a second objective function with the goal of maximizing the edge user satisfaction of the adjacent cells; converting the adjustment process of the partial frequency reuse scheme of the adjacent cells into a second Markov decision process to obtain the partial frequency reuse scheme of the adjacent cells under the condition of maximizing the edge user satisfaction of the adjacent cells, wherein the reward function of the second Markov decision process is the second objective function, the state of the second Markov decision process is the number of frequency subchannels required by the edge users of the adjacent cells, and the execution action of the second Markov decision process is the frequency subchannel allocation action.

[0024] In some embodiments, the second objective function and the corresponding constraints are expressed as follows:

[0025]

[0026] Where, T represents the total number of time slots; t represents the time slot number; represents the marginal user satisfaction of cell n covered by satellite m at time slot t; Indicates the total number of adjacent cells within the satellite; Indicates the total number of inter-satellite adjacent cells; Indicates the set of adjacent cells within the satellite; S represents the set of inter-satellite adjacent cells; n,com represents the frequency subchannel set allocated to the central user of cell n; S n,pri represents the frequency subchannel set allocated to the edge user of cell n; S n′,pri represents the frequency subchannel set allocated to the edge user of cell n′; SUM set represents the total frequency subchannel set of the system; n′ represents the adjacent cell of cell n;

[0027] Among them, the first constraint The frequency subchannel set allocated to the center user and edge user of cell n does not exceed the total frequency subchannel set of the system; the second constraint condition Indicates that the frequency subchannel sets allocated to the edge users and the center users of the cell n are isolated from each other; the third constraint n≠n′ indicates that the frequency subchannel sets allocated to edge users of cell n and cell n′ are isolated from each other.

[0028] In some embodiments, the state, action, and reward functions of the second Markov decision process are expressed as follows:

[0029]

[0030]

[0031]

[0032] in, represents the state of the second Markov decision process at time slot t; represents the number of frequency subchannels required by edge users of cell n in time slot t; n represents the cell number; N represents the set of cells that can be covered by each satellite; represents the action performed by the second Markov decision process at time slot t; Indicates frequency sub-channel selection for cell n; represents the reward value of the second Markov decision process at time slot t; represents the marginal user satisfaction of cell n covered by satellite m at time slot t; the cumulative reward value of the second Markov decision process The update rule is:

[0033]

[0034] in, represents the state of the second Markov decision process at time slot t+1; represents the execution action of the second Markov decision process at time slot t+1; R 1 t+1 represents the reward value of the second Markov decision process at time slot t+1; γ 1 represents the discount factor; α 1 Represents the learning rate.

[0035] In some embodiments, the neighboring cells include: intra-satellite neighboring cells and / or inter-satellite neighboring cells.

[0036] In some embodiments, the satellite is a low-orbit satellite.

[0037] According to another aspect of the present disclosure, a beam-hopping interference processing device in a multi-satellite scenario is also provided, including: a multi-satellite beam-hopping system model establishment module, used to establish a multi-satellite beam-hopping system model, wherein the multi-satellite beam-hopping system model includes: multiple satellites, each satellite carrying multiple beams, and each beam serving one cell; a satellite beam-hopping pattern determination module, used to determine the beam-hopping pattern of each satellite in each time slot with the goal of maximizing system throughput; a dynamic partial frequency reuse scheme determination module, used to adjust the partial frequency reuse scheme of the adjacent cells in each time slot beam-hopping pattern according to the service traffic requirements of the edge users of the adjacent cells, so as to maximize the edge user satisfaction of the adjacent cells, wherein the partial frequency reuse scheme is used to allocate frequency resources to the center users and edge users of the adjacent cells, and the edge user satisfaction is determined based on the frequency resources allocated to the edge users and the frequency resources required by the edge users; a beam-hopping interference scheme output module, used to output the partial frequency reuse scheme of the adjacent cells that maximizes the edge user satisfaction of the adjacent cells.

[0038] According to another aspect of the present disclosure, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned beam-hopping interference processing methods in a multi-satellite scenario by executing the executable instructions.

[0039] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for processing beam-hopping interference in a multi-satellite scenario described in any one of the above is implemented.

[0040] According to another aspect of the present disclosure, a computer program product is further provided, including: a computer program or instructions, which, when executed by a processor, implements any of the above-mentioned beam-hopping interference processing methods in a multi-satellite scenario.

[0041] The beam-hopping interference processing method and related equipment provided in the embodiments of the present disclosure in multi-satellite scenarios establish a multi-satellite beam-hopping system model, determine the beam-hopping pattern of each satellite in each time slot with the goal of maximizing system throughput, and adjust the partial frequency reuse scheme of the adjacent cells in each time slot beam-hopping pattern based on the service traffic requirements of users at the edge of the adjacent cells. Output is a partial frequency reuse scheme for the adjacent cells that maximizes the satisfaction of users at the edge of the adjacent cells. Through the embodiments of the present disclosure, while ensuring system throughput and meeting dynamic scheduling beams, co-channel interference can be effectively reduced, thereby improving communication quality in multi-satellite scenarios.

[0042] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0044] Figure 1 A schematic diagram of a communication system architecture according to an embodiment of the present disclosure is shown;

[0045] Figure 2 A flow chart of a beam-hopping interference processing method in a multi-satellite scenario according to an embodiment of the present disclosure is shown;

[0046] Figure 3 A flowchart of an optional beam-hopping interference processing method in a multi-satellite scenario according to an embodiment of the present disclosure is shown;

[0047] Figure 4 A flowchart of determining the beam hopping pattern of each satellite to maximize system throughput based on a reinforcement learning method in an embodiment of the present disclosure is shown;

[0048] Figure 5 A flowchart of implementing dynamic partial frequency reuse based on a reinforcement learning method in an embodiment of the present disclosure is shown;

[0049] Figure 6 A flowchart showing a specific implementation of a beam hopping interference avoidance method based on dynamic fractional frequency reuse in a multi-satellite scenario according to an embodiment of the present disclosure is shown;

[0050] Figure 7 A schematic diagram of a beam-hopping interference processing device in a multi-satellite scenario according to an embodiment of the present disclosure is shown;

[0051] Figure 8 A structural block diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0052] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0053] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0054] For ease of understanding, before introducing the embodiments of the present disclosure, several terms involved in the embodiments of the present disclosure are first explained as follows:

[0055] Beam hopping: It is a dynamic beam scheduling technology in satellite communications. It controls the beam direction of the satellite through technologies such as phased array antennas, achieves flexible switching in different time slots (time segments), and covers different ground cells on demand. It can achieve the purpose of using limited beam resources to provide communication services to more ground cells.

[0056] Beam-hopping pattern: It is a spatiotemporal planning diagram for beam-hopping scheduling, used to describe the coverage and switching logic of satellite beams in time and space, and to clarify the coverage of beams on ground cells in different time slots.

[0057] Fractional Frequency Reuse (FFR) is a resource allocation strategy used in wireless communications to balance spectrum efficiency and interference with edge users. By dividing each cell into a central area and an edge area, the central area reuses all frequency resources, while the edge area is allocated frequency subchannels that do not overlap with adjacent cell edge areas to avoid co-channel interference.

[0058] The specific implementation of the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0059] Figure 1 FIG. 1 shows an exemplary communication system architecture diagram to which the beam hopping interference processing method in a multi-satellite scenario according to an embodiment of the present disclosure can be applied. Figure 1 As shown, the system architecture may include: a first satellite 10 and a second satellite 20; wherein the first satellite 10 can cover cell 1, cell 2 and cell 3 on the ground and provide services; the second satellite 20 can cover cell 3 and cell 4 on the ground.

[0060] It should be noted that the first satellite 10 and the second satellite 20 in the embodiment of the present disclosure are only examples. In actual scenarios, more satellites may be included. The beam-hopping interference processing method in the embodiment of the present disclosure can be applied to, but not limited to, low-Earth Orbit Satellite (LOE) scenarios. In fact, the beam-hopping interference processing method in the embodiment of the present disclosure can also be applied to medium-Earth Orbit (MEO), high-Earth Orbit (GEO), satellites in other special orbits, high-altitude platforms or drones, and of course, any combination of these scenarios.

[0061] Typically, satellites transmit electromagnetic beams (referred to as beams) through antennas to achieve signal coverage of ground cells. A satellite can carry multiple antennas to form multiple beams (e.g., synchronous orbit communication satellites usually have dozens of beams), each beam pointing to a different area on the ground, thereby expanding the overall coverage of the satellite. In the disclosed embodiment, a cell refers to a specific area on the ground that is actually covered by a beam. When a beam corresponds to a cell, the boundary of the beam determines the geographical range of the cell. Satellites cover multiple cells through multiple beams, each cell corresponding to a city or region on the ground. Satellites allocate different frequencies to beams of different cells (to avoid interference), and users can communicate with the satellite through the beam in a certain cell; if beam hopping technology is used, the satellite's beam will switch to different cells at different time slots. Through a dynamic adjustment mechanism, it can cope with the current complex and changeable satellite communication environment.

[0062] Those skilled in the art will know that Figure 1 The number of satellites and cells in the embodiment is only illustrative, and any number of satellites and cells may be provided according to actual needs. This embodiment of the present disclosure does not limit this.

[0063] Under the above system architecture, an embodiment of the present disclosure provides a beam hopping interference processing method in a multi-satellite scenario, which can be executed by any electronic device with computing and processing capabilities.

[0064] In some embodiments, the beam-hopping interference processing method in a multi-satellite scenario provided in the embodiments of the present disclosure can be executed by a device carried on the first satellite or the second satellite in the above-mentioned system architecture; in other embodiments, the beam-hopping interference processing method in a multi-satellite scenario provided in the embodiments of the present disclosure can be executed by a ground device (such as a ground core network device) that communicates with the first satellite and the second satellite in the above-mentioned system architecture; in other embodiments, the beam-hopping interference processing method in a multi-satellite scenario provided in the embodiments of the present disclosure can be implemented by the above-mentioned system architecture through interaction between the device carried on the first satellite or the second satellite in the above-mentioned system architecture and the ground device.

[0065] Figure 2 A flow chart of a beam hopping interference processing method in a multi-satellite scenario according to an embodiment of the present disclosure is shown. Figure 2 As shown, the beam hopping interference processing method in a multi-satellite scenario provided in an embodiment of the present disclosure may include the following steps:

[0066] S202: Establish a multi-satellite beam-hopping system model, where the multi-satellite beam-hopping system model includes: multiple satellites, each satellite carries multiple beams, and each beam serves one cell.

[0067] It should be noted that the multi-satellite beam-hopping system model in the embodiment of the present disclosure refers to a system model composed of multiple satellites and can use beam-hopping technology to collaboratively provide communication services for ground cells. Each satellite in this system model is equipped with multiple beams, and each beam serves one cell; in different time slots, by adjusting the direction of each beam, each beam can cover different cells on the ground.

[0068] S204 , with the goal of maximizing system throughput, determining the beam hopping pattern of each satellite in each time slot.

[0069] It should be noted that in a multi-satellite beam-hopping system, the beam-hopping pattern of each satellite in each time slot is determined with the goal of maximizing the system throughput. This means that by dynamically optimizing the beam pointing scheduling (which beams serve which cells), resource allocation (time slot, frequency, power) and multi-satellite coordination strategy, the throughput of the multi-satellite beam-hopping system is maximized while satisfying interference constraints (such as co-frequency interference between adjacent cells) and satellite resource limitations (such as the number of beams and transmit power).

[0070] In one embodiment, an optimal beam scheduling strategy (i.e., outputting the beam hopping pattern for each satellite in each time slot) can be solved by establishing a mathematical model with the goal of maximizing system throughput. In specific implementations, various methods (such as greedy algorithms, genetic algorithms, particle swarm optimization, game theory methods, reinforcement learning, etc.) can be used to solve the optimal beam scheduling strategy, which is not specifically limited in this disclosure.

[0071] S206. For the adjacent cells in each time slot hopping beam pattern, adjust the partial frequency reuse scheme of the adjacent cells according to the service traffic demand of the edge users of the adjacent cells to maximize the edge user satisfaction of the adjacent cells, wherein the partial frequency reuse scheme is used to allocate frequency resources between the center users and the edge users of the adjacent cells, and the edge user satisfaction is determined based on the frequency resources allocated to the edge users and the frequency resources required by the edge users.

[0072] It should be noted that the adjacent cells in the embodiments of the present disclosure refer to two or more cells (formed by satellite beam coverage) that are geographically adjacent in the beam hopping pattern of a certain time slot. Due to the adjacent locations, the signals in the edge areas (cell boundaries) of adjacent cells are prone to interfere with each other, and the communication quality of edge users is more easily affected. Partial frequency reuse scheme is a frequency resource allocation strategy. For a certain cell, the central user (the user located in the central area of ​​the cell) is located in the center of the cell and is less interfered with by the neighboring cells. More frequency resources can be reused (even sharing part of the frequency with the central users of the neighboring cells) to improve frequency utilization; while the edge users (the users located in the edge area of ​​the cell) are located at the edge of the cell and are greatly interfered with by the neighboring cells. They need to use exclusive or low-reuse frequency resources (to reduce co-frequency interference with edge users in the neighboring cells) to give priority to ensuring communication quality. Edge user satisfaction refers to an indicator used to measure the extent to which the needs of edge users are met. In one embodiment, it can be simplified to "the ratio of frequency resources actually allocated to edge users to frequency resources required by edge users". The higher the ratio, the higher the satisfaction.

[0073] In some embodiments, the neighboring cells in the examples of the present disclosure may include: intra-satellite neighboring cells and / or inter-satellite neighboring cells.

[0074] In some embodiments, the satellite in the embodiments of the present disclosure may be, but is not limited to, a low-orbit satellite.

[0075] In specific implementation, adjusting part of the frequency reuse scheme of adjacent cells may include but is not limited to: allocating completely different frequencies to edge users of adjacent cells to avoid co-frequency interference and ensure that the allocated frequencies can be used effectively; or allocating part of the frequency resources of central users to edge users.

[0076] In the disclosed embodiment, for the adjacent cells in each time slot hopping beam pattern, the partial frequency reuse scheme of the adjacent cells is adjusted according to the service traffic demand of the edge users of the adjacent cells, so as to make the frequency resources allocated to the edge users as close as possible to the frequency resources they need. In the disclosed embodiment, by optimizing the frequency allocation rules of the adjacent cells (partial frequency reuse), the resource supply of the edge users is guaranteed while reducing interference, thereby improving the service quality (satisfaction) of the edge users. Through the disclosed embodiment, not only can the excessive occupation of resources by the central users be avoided, but also the interference can be reduced by isolating the edge frequencies of the neighboring cells.

[0077] S208 , outputting a partial frequency reuse scheme for adjacent cells that maximizes user satisfaction at the edge of adjacent cells.

[0078] It should be noted that in the embodiment of the present disclosure, by optimizing the frequency resource allocation strategy of adjacent cells (i.e., the partial frequency reuse scheme), a partial frequency reuse scheme is output that can maximize the satisfaction of users at the edge of adjacent cells. By allocating resources according to the partial frequency reuse scheme, the highest satisfaction of users at the edge of adjacent cells can be achieved.

[0079] In some embodiments, after determining the beam hopping pattern of each satellite in each time slot, it can be determined whether there are overlapping cells served by multiple satellites according to the beam hopping pattern of each satellite in each time slot. If there are overlapping cells served by multiple satellites, Figure 3 As shown, resources are reallocated for overlapping cells through the following steps:

[0080] S210 , for overlapping cells served by multiple satellites, determine a satellite with the largest signal-to-interference-plus-noise ratio among the multiple satellites as a serving satellite for the overlapping cell, and select cells with the next largest signal-to-interference-plus-noise ratio for the remaining satellites except the serving satellite.

[0081] by Figure 1 Taking the communication system shown in FIG. 1 as an example, the first satellite 10 and the second satellite 20 can both cover cell 3. It is possible that the first satellite 10 and the second satellite 20 may simultaneously provide services for cell 3. Therefore, it is necessary to reallocate the beam hopping patterns for the overlapping cells. First, the overlapping cells are found, and the signal-to-interference-plus-noise ratio (SIN) is used as the satellite selection criterion. By comparing the SIN between the cell and the satellite, the serving satellite with the highest SIN between the cell and the satellite is selected. The remaining satellites do not serve the cell, and the cell with the second highest SIN is selected for service.

[0082] In some embodiments, the beam-hopping interference processing method in a multi-satellite scenario provided in the embodiments of the present disclosure can determine the beam-hopping pattern of each satellite to maximize the system throughput based on a reinforcement learning method. For example, the optimization process of beam scheduling is regarded as a Markov decision process (MDP), and each satellite is regarded as an intelligent agent. By interacting with the environment (user needs, channel status), the mapping between "beam scheduling actions" and "throughput rewards" is learned to generate the optimal beam strategy.

[0083] As an optional embodiment, Figure 4 As shown, when using the Markov decision process to solve the optimization problem, the beam hopping interference processing method in a multi-satellite scenario provided in the embodiment of the present disclosure can determine the beam hopping pattern of each satellite to maximize the system throughput through the following steps:

[0084] S402, establishing a first objective function with the goal of maximizing system throughput;

[0085] S404: Convert the optimization problem of the first objective function into a first Markov decision process to obtain a beam hopping pattern for each satellite in each time slot that maximizes system throughput, wherein the reward function of the first Markov decision process is the first objective function, the state of the first Markov decision process is the cell environment state, and the execution action of the first Markov decision process is the beam hopping pattern selection action.

[0086] It should be noted that a Markov process is a process that maximizes the cumulative reward (also known as the Q value) by influencing state transitions through the execution of actions. In a multi-satellite beam-hopping system, each satellite's selection of a different beam switching strategy (action) will cause changes in user demand state (state), affecting system throughput (reward). Therefore, the disclosed embodiments can solve the optimal strategy through a Markov decision process.

[0087] In some embodiments, the first objective function and the corresponding constraints are expressed as follows:

[0088]

[0089]

[0090] Where T represents the total number of time slots; t represents the time slot number; M represents the total number of satellites; m represents the satellite number; N represents the total number of cells that each satellite can cover; n represents the cell number; represents the throughput of satellite m serving cell n in time slot t, represents the channel capacity of satellite m covering cell n in time slot t, is the service demand of cell n covered by satellite m in time slot t;

[0091] Among them, the first constraint t∈T represents the number of cells served within the satellite coverage area in each time slot The number of effective beam payloads K carried by the satellite does not exceed the second constraint. t∈T represents the power allocated to all cells within the satellite coverage area in each time slot Not exceeding the total power P of the satellite tot ; M represents the satellite set; T represents the time slot set; θ m Represents the set of cells covered by satellite m.

[0092] In some embodiments, the state, action, and reward functions of the first Markov decision process are expressed as follows:

[0093]

[0094] Among them, s trepresents the state of the first Markov decision process at time slot t; represents the environmental status information of cell n at time slot t; n represents the cell number; N represents the set of cells that can be covered by each satellite; m represents the satellite number; M represents the satellite set; θ m represents the set of cells covered by satellite m; a t represents the action performed by the first Markov decision process at time slot t; represents the beam hopping pattern selection; R t represents the reward value of the first Markov decision process at time slot t; represents the throughput of satellite m serving cell n in time slot t; the cumulative reward value Q(s t ,a t ) is updated as follows:

[0095]

[0096] Among them, s t+1 represents the state of the first Markov decision process at time slot t+1; a t+1 represents the execution action of a Markov decision process at time slot t+1; R t+1 represents the reward value of the first Markov decision process at time slot t+1; γ represents the discount factor; α represents the learning rate.

[0097] In some embodiments, the beam hopping interference processing method in a multi-satellite scenario provided in the embodiments of the present disclosure can realize dynamic partial frequency reuse by using a reinforcement learning method. Specifically, Figure 5 As shown, the following steps may be included:

[0098] S502, establishing a second objective function with the goal of maximizing the satisfaction of edge users in adjacent cells;

[0099] S504: Convert the adjustment process of the partial frequency reuse scheme of the adjacent cell into a second Markov decision process to obtain the partial frequency reuse scheme of the adjacent cell that maximizes the satisfaction of the users at the edge of the adjacent cell, wherein the reward function of the second Markov decision process is the second objective function, the state of the second Markov decision process is the number of frequency subchannels required by the users at the edge of the adjacent cell, and the execution action of the second Markov decision process is the frequency subchannel allocation action.

[0100] In some embodiments, the second objective function and corresponding constraints are expressed as follows:

[0101]

[0102] Where, T represents the total number of time slots; t represents the time slot number; represents the marginal user satisfaction of cell n covered by satellite m at time slot t; Indicates the total number of adjacent cells within the satellite; Indicates the total number of inter-satellite adjacent cells; Indicates the set of adjacent cells within the satellite; S represents the set of inter-satellite adjacent cells; n,com represents the frequency subchannel set allocated to the central user of cell n; S n,pri represents the frequency subchannel set allocated to the edge user of cell n; S n′,pri represents the frequency subchannel set allocated to the edge user of cell n′; SUM set represents the total frequency subchannel set of the system; n′ represents the adjacent cell of cell n;

[0103] Among them, the first constraint The frequency subchannel set allocated to the center user and edge user of cell n does not exceed the total frequency subchannel set of the system; the second constraint condition Indicates that the frequency subchannel sets allocated to the edge users and the center users of the cell n are isolated from each other; the third constraint n≠n′ indicates that the frequency subchannel sets allocated to edge users of cell n and cell n′ are isolated from each other.

[0104] In some embodiments, the state, action, and reward functions of the second Markov decision process are expressed as follows:

[0105]

[0106] in, represents the state of the second Markov decision process at time slot t; represents the number of frequency subchannels required by edge users of cell n in time slot t; n represents the cell number; N represents the set of cells that can be covered by each satellite; represents the action performed by the second Markov decision process at time slot t; Indicates frequency sub-channel selection for cell n; represents the reward value of the second Markov decision process at time slot t; represents the marginal user satisfaction of cell n covered by satellite m at time slot t; the cumulative reward value of the second Markov decision process The update rule is:

[0107]

[0108] in, represents the state of the second Markov decision process at time slot t+1; represents the execution action of the second Markov decision process at time slot t+1; R 1t+1 represents the reward value of the second Markov decision process at time slot t+1; γ 1 represents the discount factor; α 1 Represents the learning rate.

[0109] Figure 6 A specific implementation flow chart of a beam hopping interference avoidance method based on dynamic partial frequency reuse in a multi-satellite scenario according to an embodiment of the present disclosure is shown. Figure 6 As shown, specifically including:

[0110] S602: Establish a multi-satellite beam-hopping system model, define system parameters, and initialize the environment.

[0111] Specifically, after establishing a multi-satellite beam-hopping system model, an objective function is established with the goal of maximizing the system throughput.

[0112] S604: Execute a beam hopping pattern selection action and update a beam hopping pattern selection Q value table.

[0113] Specifically, reinforcement learning is introduced to solve the optimization problem, which is modeled as a Markov decision process. The state is defined as the cell channel gain, the action is defined as the beam hopping pattern selection, and the reward function is defined as related to the throughput. The beam hopping pattern selection action is executed and the beam hopping pattern selection Q-value table is updated until the beam hopping pattern of each satellite is obtained to maximize the system throughput.

[0114] S606: Output the beam hopping pattern of each satellite.

[0115] S608: Reallocate beam hopping patterns for overlapping cells.

[0116] Specifically, services are reallocated to overlapping cells according to the beam hopping pattern. The signal-to-interference-plus-noise ratio (SIN) is used as the satellite selection criterion. By comparing the cell and the satellite with the largest SIN, the service satellite is selected. The remaining satellites do not serve the cell, and the cell with the second largest SIN is selected for service.

[0117] S610: Allocate frequency sub-channels and update the partial frequency reuse Q value table.

[0118] Specifically, for the intra-satellite adjacent cells and inter-satellite adjacent cells in the beam hopping pattern, an objective function is established with the goal of maximizing the user satisfaction at the edge of the adjacent cell. Reinforcement learning is introduced to solve the optimization problem. The state is defined as the number of frequency subchannel requirements in the cell edge area, the action is defined as the cell frequency subchannel selection, and the reward function is defined as related to the user satisfaction at the edge of the system. The action of allocating frequency subchannels is executed and the partial frequency reuse Q value table is updated.

[0119] S612, set t=t+1, and jump to the next time slot.

[0120] S614, determine whether the system convergence condition is met; if so, execute S616; if not, return to execute S604.

[0121] S616: Output a multi-satellite beam hopping interference avoidance solution based on dynamic fractional frequency reuse.

[0122] Taking low-orbit satellites as an example, assuming there are M low-orbit satellites, each satellite can cover N cells. In a multi-satellite beam-hopping system, each satellite carries K effective beam payloads in each time slot, that is, each satellite serves K cells in each time slot, where K < N. The low-orbit satellites, the cells covered by the satellites, the time slots, and the cells covered by each satellite are represented by the set:

[0123] M={1,2,...,m,...,M} (13)

[0124] N={1,2,...,n,...,N} (14)

[0125] T={1,2,...,t,...,T} (15)

[0126] θ={θ1,θ2,...,θ m ...,θ M} (16)

[0127] In a multi-satellite system, there may be inter-satellite co-frequency interference and intra-satellite co-frequency interference. In time slot t, the signal-to-interference-and-noise ratio of cell n covered by satellite m can be expressed as:

[0128]

[0129] in is the power allocated to cell n in time slot t under the coverage of satellite m, is the channel gain of cell n at time slot t under the coverage of satellite m, is the power allocated to other co-frequency cells n′ under the same satellite m in time slot t, is the channel gain of other co-frequency cells n′ under the same satellite m at time slot t, θ mm′ is the set of overlapping coverage cells between satellite m and satellite m′, is the power allocated to other co-frequency cells n′ in time slot t under different satellites m′, is the channel gain of other co-frequency cells n′ at time slot t under different satellites m′, is the additive white Gaussian noise power.

[0130] The channel capacity of cell n in time slot t under the coverage of satellite m can be expressed as:

[0131]

[0132] in, Indicates that cell n is served by satellite m, otherwise it is not served. m is the bandwidth of satellite m, then the throughput of satellite m can be expressed as in is the service demand of cell n in time slot t under the coverage of satellite m.

[0133] With the goal of maximizing system throughput, we establish the objective function shown in formula (1) and the constraints shown in formula (2). The first constraint states that the number of cells served within the satellite coverage area in each time slot does not exceed the number of satellite effective beam payloads, and the second constraint states that the power allocated to all cells within the satellite coverage area in each time slot does not exceed the total satellite power.

[0134] Reinforcement learning is introduced to solve the optimization problem, modeled as a Markov decision process. The state is defined as shown in Equation (3), the action is defined as shown in Equation (4), and the reward function is defined as shown in Equation (5). Higher system throughput results in greater rewards. The beam-hopping pattern for each satellite in each time slot is obtained using the Q-value update rule shown in Equation (6).

[0135] In a multi-satellite scenario, multiple satellites may overlap to serve the same cell. Therefore, it is necessary to reallocate the beam hopping patterns for the overlapping cells. First, find the overlapping cells and use the signal-to-interference-plus-noise ratio (SIN) as the satellite selection criterion. By comparing the SIN between the cell and the satellite, the serving satellite with the highest SIN is selected. The remaining satellites do not serve the cell, and the cell with the second highest SIN is selected for service.

[0136] For the intra-satellite adjacent cells and inter-satellite adjacent cells in the down-hopping beam pattern of this time slot, the number of intra-satellite adjacent cells and inter-satellite adjacent cells is first counted as follows: Each pair of adjacent intra-satellite cells and adjacent inter-satellite cells are put into the intra-satellite adjacent cell set and the inter-satellite adjacent cell set respectively.

[0137] The set of adjacent cells within a satellite is:

[0138]

[0139] The inter-satellite adjacent cell set is:

[0140]

[0141] According to the distance between the cell users and the beam center, the cell edge users and the center users are divided and included in the edge user set respectively. and central user set For adjacent cells n and n′, the total available bandwidth of the system is B m, suppose the sub-channel bandwidth for frequency band allocation in the system is B min , then the total number of sub-channels is expressed as follows:

[0142]

[0143] The frequency subchannel set number is expressed as:

[0144] SUM set ={s|s=1,2,...,SUM} (22)

[0145] The frequency band allocation of adjacent cell n in time slot t under the coverage of satellite m is expressed as:

[0146]

[0147] in Indicates that the sth frequency subchannel is not allocated to cell n. Indicates that the sth frequency subchannel is allocated to cell n and is used as a common subband for users in the center of cell n. The sth frequency subchannel is allocated to cell n and used as a private subband for users at the edge of cell n. The partial frequency reuse scheme between adjacent cells n and n′ can be expressed as:

[0148]

[0149] When m=m′, it refers to the adjacent cells within the satellite, and when m≠m′, it refers to the adjacent cells between satellites. In order to obtain a dynamic partial frequency reuse scheme, reinforcement learning is used for training and solution.

[0150] First, define the marginal user satisfaction of cell n in time slot t under the coverage of satellite m as:

[0151]

[0152] in Indicates the number of frequency subchannels allocated to cell n for use as private subbands. Denotes the number of frequency subchannels required by edge user u in cell n at time slot t. To maximize the satisfaction of users at the edge of adjacent cells, we establish the objective function shown in formula (7) and the constraints shown in formula (8). The first constraint states that the set of frequency subchannels allocated to a cell does not exceed the total set of frequency subchannels in the system. The second constraint states that the frequency subchannels allocated to the cell edge and the center are isolated from each other. The third constraint states that the frequency subchannels allocated to the cell and the cell edge are isolated from each other.

[0153] Reinforcement learning is introduced to solve the optimization problem, which is modeled as a Markov decision process. The state shown in formula (9), the action shown in formula (10), and the reward function shown in formula (11) are defined. The higher the satisfaction of the edge users of the system, the more rewards they receive. The Q value update rule shown in formula (12) is used to obtain the partial frequency reuse scheme for adjacent cells. According to the partial frequency reuse scheme, frequency sub-channels are allocated to adjacent cells, and low-orbit satellite beam hopping interference is avoided based on dynamic partial frequency reuse in multi-satellite scenarios, reducing co-channel interference, improving the signal-to-noise ratio of edge users, and improving the overall system throughput.

[0154] A system model is designed for a multi-satellite beam-hopping scenario. The method provided in the embodiments of the present disclosure obtains each satellite-hopping beam pattern with the goal of maximizing system throughput, and then reallocates overlapping inter-satellite cells. For the served intra-satellite adjacent cells and inter-satellite adjacent cells, a dynamic partial frequency reuse method based on reinforcement learning is used to ensure that the frequency band allocation of edge area users meets the dynamic changes in traffic requests, reduce co-channel interference, and at the same time improve the signal-to-noise ratio of edge users, thereby improving the overall system throughput.

[0155] Based on the same inventive concept, the present disclosure also provides a device for processing beam-hopping interference in a multi-satellite scenario, as described in the following embodiments. Because the principles for solving the problem in this device embodiment are similar to those in the aforementioned method embodiment, the implementation of this device embodiment can refer to the implementation of the aforementioned method embodiment, and any repetitions will not be repeated.

[0156] Figure 7 A schematic diagram of a beam hopping interference processing device in a multi-satellite scenario according to an embodiment of the present disclosure is shown. Figure 7 As shown, the apparatus includes: a multi-satellite beam-hopping system model building module 701, a satellite beam-hopping pattern determining module 702, a dynamic fractional frequency reuse scheme determining module 703 and a beam-hopping interference scheme output module 704.

[0157] Among them, a multi-satellite beam-hopping system model establishment module 701 is used to establish a multi-satellite beam-hopping system model, wherein the multi-satellite beam-hopping system model includes: multiple satellites, each satellite carries multiple beams, and each beam serves one cell; a satellite beam-hopping pattern determination module 702 is used to determine the beam-hopping pattern of each satellite in each time slot with the goal of maximizing system throughput; a dynamic partial frequency reuse scheme determination module 703 is used to adjust the partial frequency reuse scheme of the adjacent cells in each time slot beam-hopping pattern according to the service traffic requirements of the edge users of the adjacent cells to maximize the edge user satisfaction of the adjacent cells, wherein the partial frequency reuse scheme is used to allocate frequency resources to the central users and edge users of the adjacent cells, and the edge user satisfaction is determined based on the frequency resources allocated to the edge users and the frequency resources required by the edge users; a beam-hopping interference scheme output module 704 is used to output the partial frequency reuse scheme of the adjacent cells that maximizes the edge user satisfaction of the adjacent cells.

[0158] In some embodiments, the neighboring cells include: intra-satellite neighboring cells and / or inter-satellite neighboring cells.

[0159] In some embodiments, the satellite is a low-orbit satellite.

[0160] In some embodiments, the beam-hopping interference processing apparatus in a multi-satellite scenario provided in the embodiments of the present disclosure may further include: an overlapping cell resource allocation module 705, configured to, for overlapping cells served by multiple satellites, determine a satellite with the largest signal-to-interference-plus-noise ratio among the multiple satellites as a serving satellite for the overlapping cell, and select a cell with the second largest signal-to-interference-plus-noise ratio for service for the remaining satellites among the multiple satellites except the serving satellite.

[0161] In some embodiments, the satellite beam hopping pattern determination module 702 is further used to: establish a first objective function with the goal of maximizing system throughput; convert the optimization problem of the first objective function into a first Markov decision process to obtain the beam hopping pattern of each satellite in each time slot under the maximum system throughput, wherein the reward function of the first Markov decision process is the first objective function, the state of the first Markov decision process is the cell environment state, and the execution action of the first Markov decision process is the beam hopping pattern selection action.

[0162] In some embodiments, the dynamic partial frequency reuse scheme determination module 703 is further used to: establish a second objective function with the goal of maximizing the satisfaction of edge users of adjacent cells; convert the adjustment process of the partial frequency reuse scheme of the adjacent cells into a second Markov decision process to obtain the partial frequency reuse scheme of the adjacent cells under the condition of maximizing the satisfaction of edge users of adjacent cells, wherein the reward function of the second Markov decision process is the second objective function, the state of the second Markov decision process is the number of frequency subchannels required by edge users of adjacent cells, and the execution action of the second Markov decision process is the frequency subchannel allocation action.

[0163] It should be noted that the examples and application scenarios implemented by the modules in the above-mentioned apparatus embodiment are the same as those implemented by the corresponding steps in the method embodiment, but are not limited to the contents disclosed in the above-mentioned method embodiment. It should be noted that the above-mentioned modules, as part of the apparatus, can be executed in a computer system, such as a set of computer-executable instructions.

[0164] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation that combines hardware and software aspects, which may be collectively referred to herein as a "circuit," "module," or "system."

[0165] Based on the same inventive concept, an embodiment of the present disclosure further provides an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any of the aforementioned beam-hopping interference processing methods in a multi-satellite scenario by executing the executable instructions. Because the principles for solving the problem in this electronic device embodiment are similar to those in the aforementioned method embodiment, the implementation of this electronic device embodiment can refer to the implementation of the aforementioned method embodiment, and any repetitive details will not be repeated.

[0166] Refer to the following Figure 8 800 according to this embodiment of the present disclosure will be described. Figure 8 The electronic device 800 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0167] like Figure 8 As shown, electronic device 800 is implemented as a general-purpose computing device. Components of electronic device 800 may include, but are not limited to, the aforementioned at least one processing unit 810, the aforementioned at least one storage unit 820, and a bus 830 connecting various system components (including storage unit 820 and processing unit 810).

[0168] The storage unit stores a program code, and the program code can be executed by the processing unit 810, so that the processing unit 810 performs the steps described in the "Exemplary Method" section of this specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 810 can perform the following steps of the above method embodiment: establishing a multi-satellite hopping beam system model, wherein the multi-satellite hopping beam system model includes: multiple satellites, each satellite carries multiple beams, and each beam serves one cell; with the goal of maximizing system throughput, determining the hopping beam pattern of each satellite in each time slot; for the adjacent cells in each time slot hopping beam pattern, adjusting the partial frequency reuse scheme of the adjacent cells according to the service traffic demand of the edge users of the adjacent cells to maximize the edge user satisfaction of the adjacent cells, wherein the partial frequency reuse scheme is used to allocate frequency resources to the center users and edge users of the adjacent cells, and the edge user satisfaction is determined based on the frequency resources allocated to the edge users and the frequency resources required by the edge users; outputting the partial frequency reuse scheme of the adjacent cells that maximizes the edge user satisfaction of the adjacent cells.

[0169] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 8201 and / or a cache memory unit 8202 , and may further include a read-only memory unit (ROM) 8203 .

[0170] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0171] Bus 830 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0172] The electronic device 800 can also communicate with one or more external devices 840 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 800, and / or any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 850. Furthermore, the electronic device 800 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 860. As shown, the network adapter 860 communicates with other modules of the electronic device 800 via a bus 830. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 800, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0173] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0174] Based on the same inventive concept, embodiments of the present disclosure also provide a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements any of the aforementioned methods for handling beam-hopping interference in a multi-satellite scenario. Because the principles underlying the problems addressed by this computer-readable storage medium embodiment are similar to those of the aforementioned method embodiment, the implementation of this computer-readable storage medium embodiment can be referenced to the implementation of the aforementioned method embodiment, and any repetitive details will not be repeated.

[0175] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0176] In the present disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0177] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0178] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a standalone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0179] Based on the same inventive concept, embodiments of the present disclosure further provide a computer program product, including a computer program or instructions. When executed by a processor, the computer program or instructions implement the beam-hopping interference processing method for a multi-satellite scenario described in any of the aforementioned method embodiments. Because the principles underlying the problem solved by this computer program product embodiment are similar to those of the aforementioned method embodiments, the implementation of this computer program product embodiment can be referenced to the implementation of the aforementioned method embodiments, and any repetitive details will not be repeated.

[0180] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0181] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0182] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0183] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims

1. A beam hopping interference processing method in a multi-satellite scenario, characterized in that: include: Establishing a multi-satellite beam-hopping system model, wherein the multi-satellite beam-hopping system model includes: multiple satellites, each satellite carrying multiple beams, and each beam serving one cell; To maximize system throughput, the beam hopping pattern of each satellite in each time slot is determined; For the adjacent cells in each time slot hopping beam pattern, adjusting a fractional frequency reuse scheme of the adjacent cells according to the traffic demand of edge users of the adjacent cells to maximize the satisfaction of edge users of the adjacent cells, wherein the fractional frequency reuse scheme is used to allocate frequency resources between center users and edge users of the adjacent cells, and the satisfaction of edge users is determined based on the frequency resources allocated to the edge users and the frequency resources required by the edge users; Output the partial frequency reuse scheme of adjacent cells that maximizes the user satisfaction at the edge of adjacent cells.

2. The beam hopping interference processing method in a multi-satellite scenario according to claim 1, characterized in that: After determining the beam hopping pattern of each satellite in each time slot with the goal of maximizing system throughput, the method further includes: For overlapping cells served by multiple satellites, a satellite with the largest signal to interference plus noise ratio among the multiple satellites is determined as a serving satellite for the overlapping cell, and cells with the second largest signal to interference plus noise ratio are selected for service by the remaining satellites except the serving satellite.

3. The beam hopping interference processing method in a multi-satellite scenario according to claim 1, characterized in that: To maximize system throughput, the beam hopping pattern of each satellite in each time slot is determined, including: With the goal of maximizing system throughput, the first objective function is established; The optimization problem of the first objective function is converted into a first Markov decision process to obtain a beam hopping pattern for each satellite in each time slot under maximum system throughput, wherein the reward function of the first Markov decision process is the first objective function, the state of the first Markov decision process is the cell environment state, and the execution action of the first Markov decision process is a beam hopping pattern selection action.

4. The beam hopping interference processing method in a multi-satellite scenario according to claim 3, characterized in that: The first objective function and the corresponding constraints are expressed as follows: Where T represents the total number of time slots; t represents the time slot number; M represents the total number of satellites; m represents the satellite number; N represents the total number of cells that each satellite can cover; n represents the cell number; represents the throughput of satellite m serving cell n in time slot t, represents the channel capacity of satellite m covering cell n in time slot t, is the service demand of cell n covered by satellite m in time slot t; Among them, the first constraint t∈T represents the number of cells served within the satellite coverage area in each time slot The number of effective beam payloads K carried by the satellite does not exceed the second constraint. t∈T represents the power allocated to all cells within the satellite coverage area in each time slot Not exceeding the total power P of the satellite tot ; M represents the satellite set; T represents the time slot set; θ m Represents the set of cells covered by satellite m.

5. The beam hopping interference processing method in a multi-satellite scenario according to claim 3, characterized in that: The state, action and reward functions of the first Markov decision process are expressed as follows: Among them, s t represents the state of the first Markov decision process at time slot t; represents the environmental status information of cell n at time slot t; n represents the cell number; N represents the set of cells that can be covered by each satellite; m represents the satellite number; M represents the satellite set; θ m represents the set of cells covered by satellite m; a t represents the action performed by the first Markov decision process at time slot t; represents the beam hopping pattern selection; R t represents the reward value of the first Markov decision process at time slot t; represents the throughput of satellite m serving cell n in time slot t; the cumulative reward value Q(s t ,a t ) is updated as follows: Among them, s t+1 represents the state of the first Markov decision process at time slot t+1; a t+1 represents the execution action of a Markov decision process at time slot t+1; R t+1 represents the reward value of the first Markov decision process at time slot t+1; γ represents the discount factor; α represents the learning rate.

6. The beam hopping interference processing method in a multi-satellite scenario according to claim 1, characterized in that: For each adjacent cell in the time slot hopping beam pattern, adjusting a partial frequency reuse scheme of the adjacent cell according to a service flow demand of an edge user of the adjacent cell to maximize edge user satisfaction of the adjacent cell, including: The second objective function is established with the goal of maximizing the satisfaction of edge users in adjacent cells; The adjustment process of the partial frequency reuse scheme of the adjacent cell is converted into a second Markov decision process to obtain a partial frequency reuse scheme of the adjacent cell that maximizes the satisfaction of users at the edge of the adjacent cell, wherein the reward function of the second Markov decision process is the second objective function, the state of the second Markov decision process is the number of frequency subchannels required by users at the edge of the adjacent cell, and the execution action of the second Markov decision process is a frequency subchannel allocation action.

7. The beam hopping interference processing method in a multi-satellite scenario according to claim 6, characterized in that: The second objective function and the corresponding constraints are expressed as follows: Where, T represents the total number of time slots; t represents the time slot number; represents the marginal user satisfaction of cell n covered by satellite m at time slot t; Indicates the total number of adjacent cells within the satellite; Indicates the total number of inter-satellite adjacent cells; Indicates the set of adjacent cells within the satellite; S represents the set of inter-satellite adjacent cells; n,com represents the frequency subchannel set allocated to the central user of cell n; S n,pri represents the frequency subchannel set allocated to the edge user of cell n; S n′,pri represents the frequency subchannel set allocated to the edge user of cell n′; SUM set represents the total frequency subchannel set of the system; n′ represents the adjacent cell of cell n; Among them, the first constraint The frequency subchannel set allocated to the center user and edge user of cell n does not exceed the total frequency subchannel set of the system; the second constraint condition Indicates that the frequency subchannel sets allocated to the edge users and the center users of the cell n are isolated from each other; the third constraint n≠n′ indicates that the frequency subchannel sets allocated to edge users of cell n and cell n′ are isolated from each other.

8. The beam hopping interference processing method in a multi-satellite scenario according to claim 6, characterized in that: The state, action and reward functions of the second Markov decision process are expressed as follows: in, represents the state of the second Markov decision process at time slot t; represents the number of frequency subchannels required by edge users of cell n in time slot t; n represents the cell number; N represents the set of cells that can be covered by each satellite; represents the action performed by the second Markov decision process at time slot t; Indicates frequency sub-channel selection for cell n; represents the reward value of the second Markov decision process at time slot t; represents the marginal user satisfaction of cell n covered by satellite m at time slot t; the cumulative reward value of the second Markov decision process The update rule is: in, represents the state of the second Markov decision process at time slot t+1; represents the execution action of the second Markov decision process at time slot t+1; R 1 t+1 represents the reward value of the second Markov decision process at time slot t+1; γ 1 represents the discount factor; α 1 Represents the learning rate.

9. The beam hopping interference processing method in a multi-satellite scenario according to any one of claims 1 to 8, characterized in that: The adjacent cells include: intra-satellite adjacent cells and / or inter-satellite adjacent cells.

10. The beam hopping interference processing method in a multi-satellite scenario according to any one of claims 1 to 8, characterized in that: The satellite is a low-orbit satellite.

11. A beam hopping interference processing device in a multi-satellite scenario, characterized in that: include: A multi-satellite beam-hopping system model establishment module is used to establish a multi-satellite beam-hopping system model, wherein the multi-satellite beam-hopping system model includes: multiple satellites, each satellite carries multiple beams, and each beam serves one cell; A satellite beam hopping pattern determination module is used to determine the beam hopping pattern of each satellite in each time slot with the goal of maximizing system throughput; a dynamic fractional frequency reuse scheme determination module, configured to adjust, for each adjacent cell in a time slot hopping beam pattern, a fractional frequency reuse scheme of the adjacent cell according to a traffic flow demand of an edge user of the adjacent cell, so as to maximize the edge user satisfaction of the adjacent cell, wherein the fractional frequency reuse scheme is used to allocate frequency resources between a center user and an edge user of the adjacent cell, and the edge user satisfaction is determined based on the frequency resources allocated to the edge user and the frequency resources demanded by the edge user; The beam hopping interference scheme output module is used to output a partial frequency reuse scheme for adjacent cells that maximizes user satisfaction at the edge of adjacent cells.

12. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to execute the beam-hopping interference processing method in a multi-satellite scenario according to any one of claims 1 to 10 by executing the executable instructions.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the beam-hopping interference processing method in a multi-satellite scenario according to any one of claims 1 to 10 is implemented.

14. A computer program product comprising: A computer program or instruction, characterized in that when the computer program or instruction is executed by a processor, it implements the beam hopping interference processing method in a multi-satellite scenario according to any one of claims 1 to 10.

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