Spectrum sharing method and system based on new networking of aggregation stations and AI base stations

Abstract

The present disclosure provides a spectrum sharing method and system based on a new type of networking of aggregation stations and AI base stations, which aims to solve the interference problem existing in spectrum sharing. The method includes: determining a networking architecture based on aggregation stations and AI base stations; based on the networking architecture, determining a networking system model based on aggregation stations and AI base stations; the networking system model includes a periodically updated radio electromagnetic map; based on the radio electromagnetic map included in the networking system model, determining a resource configuration scheme for the current time slot; the resource configuration scheme for the current time slot is determined with the goal of maximizing the minimum terminal capacity in the networking architecture; the terminal capacity represents: the ability of the terminal to transmit data; based on the resource configuration scheme, determining an anti-interference scheme for reducing spectrum sharing interference in the current time slot.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless communication technology, and in particular to a spectrum sharing method and system based on a novel networking of a convergence station and an AI base station. Background Art

[0002] Aggregation stations are new, relatively tall, elevated wireless backhaul sites, while AI base stations are new, flexible and mobile base stations equipped with edge computing capabilities. A comprehensive comparison of performance, including coverage, survivability, latency, spectrum efficiency, and power efficiency, shows that aggregation stations are more cost-effective and easier to maintain than traditional satellite networks. AI base station networking offers greater flexibility than terrestrial networks, enabling flexible, on-demand deployment across multiple locations and platforms.

[0003] However, there are still many technical issues to be resolved in the networking of aggregation stations and AI base stations. Among them, the most serious problem is that while spectrum sharing based on the networking of aggregation stations and AI base stations brings high spectrum reuse gain to network users, it also brings complex mixed co-channel interference to the network. Summary of the Invention

[0004] To overcome the problems existing in related technologies, the present disclosure provides a spectrum sharing method and system based on a new type of network of aggregation stations and AI base stations. The technical solutions of the present disclosure are as follows:

[0005] According to a first aspect of an embodiment of the present disclosure, a spectrum sharing method based on a novel network of a convergence station and an AI base station is provided, including:

[0006] Determine the networking architecture based on aggregation stations and AI base stations;

[0007] Determining a networking system model based on a convergence station and an AI base station according to the networking architecture; the networking system model includes a periodically updated radio electromagnetic map;

[0008] Determining a resource allocation scheme for a current time slot based on a radio electromagnetic map included in the networking system model; wherein the resource allocation scheme for the current time slot is determined with the goal of maximizing the minimum terminal capacity in the networking architecture; wherein the terminal capacity represents: the ability of a terminal to transmit data;

[0009] According to the resource configuration scheme, an anti-interference scheme for reducing spectrum sharing interference in the current time slot is determined.

[0010] Optionally, the networking architecture includes a network controller, and determining a resource configuration scheme for a current time slot according to a radio electromagnetic map included in the networking system model includes:

[0011] Moving the position of the AI ​​base station in the networking system model;

[0012] Obtaining the radio electromagnetic map updated in real time according to the real-time movement of the AI ​​base station in the networking system model;

[0013] Calculating the terminal capacity of each terminal by combining the wireless electromagnetic map updated in real time with the network controller;

[0014] According to the terminal fairness policy, determining the terminal with the smallest terminal capacity among the terminals as the target terminal;

[0015] updating the target terminal in real time according to the movement of the AI ​​base station, and determining the target position of each AI base station in the current time slot when the terminal capacity of the target terminal reaches a maximum value;

[0016] A resource configuration scheme for the current time slot is determined according to the target position of the AI ​​base station in the current time slot.

[0017] Optionally, the capacity of each terminal is calculated by the network controller in combination with the radio electromagnetic map updated in real time, including:

[0018] Determining, based on the radio electromagnetic map, a first signal-to-noise ratio corresponding to each of the aggregation stations, and determining a second signal-to-noise ratio corresponding to each of the AI ​​base stations;

[0019] Determining a first total throughput of the communication link from the aggregation station to the AI ​​base station based on each of the first signal-to-noise ratios; wherein the first total throughput represents: a data transmission capacity of the communication link from each of the aggregation stations to each of the AI ​​base stations;

[0020] Determining a second total throughput of the communication link from the AI ​​base station to the terminal based on each of the second signal-to-noise ratios; wherein the second total throughput represents: a data transmission capacity of the communication link from each of the AI ​​base stations to each of the terminals;

[0021] Determining the number of terminals included in the networking architecture;

[0022] determining a first capacity according to the first total throughput and the number of terminals;

[0023] Determining, according to the second total throughput, a second capacity of the AI ​​base station to each of the terminals;

[0024] A smaller value between the first capacity and the second capacity is determined as the terminal capacity of each of the terminals.

[0025] Optionally, the resource configuration scheme includes a physical location of the AI ​​base station in the current time slot. When spectrum sharing is performed in conjunction with a satellite network, determining an anti-interference scheme for reducing spectrum sharing interference in the current time slot based on the resource configuration scheme includes:

[0026] Determine the interference type of spectrum sharing interference; the interference type includes intra-system interference and inter-system interference; the intra-system interference is characterized by: interference between the aggregation station and the AI ​​base station in the networking architecture; the inter-system interference is characterized by: interference between the networking architecture and the satellite network;

[0027] Determining the physical location of the AI ​​base station in the current time slot in the resource configuration scheme, and determining the antenna pattern of the aggregation station in the current time slot;

[0028] Adjusting the aggregation station communicating with the AI ​​base station based on the geographical location relationship between the aggregation station and the AI ​​base station; reducing interference within the system by adjusting the aggregation station communicating with the AI ​​base station;

[0029] According to the geographical location information and the resource allocation plan, the timing when the inter-system interference exceeds the interference threshold is predicted; and the inter-system interference is reduced through interference threshold constraints.

[0030] Optionally, adjusting the aggregation station communicating with the AI ​​base station according to a geographical location relationship between the aggregation station and the AI ​​base station includes:

[0031] Determining a geographical location relationship between each of the AI ​​base stations and each of the aggregation stations; the geographical location relationship includes obstacle information;

[0032] Determine the aggregation station that has no obstacles between it and the AI ​​base station as the first aggregation station, and adjust the AI ​​base station to communicate with the first aggregation station;

[0033] In the case that there are multiple first aggregation stations, the first aggregation station closest to the AI ​​base station among the multiple first aggregation stations is determined as the second aggregation station, and the AI ​​base station is adjusted to communicate with the second aggregation station.

[0034] Optionally, the networking architecture includes a knowledge base, and determining a networking system model based on a convergence station and an AI base station according to the networking architecture includes:

[0035] Determining a first coverage radius of the aggregation station based on a phased array multi-beam antenna used by the aggregation station in the networking architecture; determining, based on the first coverage radius, a first core factor of the aggregation station included in the first coverage radius by the networking system model;

[0036] Determining a second coverage radius of the AI ​​base station based on a mobile antenna used by the AI ​​base station in the networking architecture; determining, based on the second coverage radius, a second core factor of the AI ​​base station included in the second coverage radius by the networking system model;

[0037] Among them, the terminals covered by each AI base station upload their corresponding terminal information to the knowledge base through the AI ​​base station; the knowledge base determines the radio electromagnetic map corresponding to the networking system model through the terminal information, the first core factor and the second core factor.

[0038] Optionally, it also includes:

[0039] The networking architecture based on aggregation stations and AI base stations uses directional antenna communication to achieve wide-area coverage without interfering with the satellite network.

[0040] By adopting directional antenna communication to achieve wide-area coverage, spectrum utilization and power efficiency are improved.

[0041] According to a second aspect of an embodiment of the present disclosure, a spectrum sharing system based on a novel network of a convergence station and an AI base station is provided, which is applied to the spectrum sharing method based on the novel network of a convergence station and an AI base station described in the first aspect, including:

[0042] Multiple aggregation stations, each of which is used to provide services to the AI ​​base station;

[0043] Multiple AI base stations are used to provide services to terminals. Terminals covered by each AI base station can communicate through the AI ​​base station.

[0044] The knowledge base is used to generate corresponding radio electromagnetic maps based on the information of each device in the network architecture.

[0045] The network controller is used to calculate the corresponding resource allocation plan based on the radio electromagnetic map.

[0046] Optionally, it also includes:

[0047] The network architecture corresponding to the new network supports 4G / 5G coordinated deployment;

[0048] Through the 4G / 5G coordinated deployment of the networking architecture, the terminal can seamlessly switch between the existing base station and the AI ​​base station in the networking architecture.

[0049] According to a third aspect of an embodiment of the present disclosure, a spectrum sharing apparatus based on a novel network of a convergence station and an AI base station is provided, including:

[0050] The network architecture determination module is used to determine the network architecture based on the aggregation station and the AI ​​base station;

[0051] A networking system model determination module, configured to determine a networking system model based on a convergence station and an AI base station according to the networking architecture; the networking system model includes a periodically updated radio electromagnetic map;

[0052] a resource allocation scheme determining module, configured to determine a resource allocation scheme for a current time slot based on a radio electromagnetic map included in the networking system model; the resource allocation scheme for the current time slot is determined with the goal of maximizing the minimum terminal capacity in the networking architecture; the terminal capacity represents the ability of a terminal to transmit data;

[0053] The anti-interference scheme determining module is used to determine an anti-interference scheme for reducing spectrum sharing interference in the current time slot according to the resource configuration scheme.

[0054] According to a fourth aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the spectrum sharing method based on the new networking of aggregation stations and AI base stations as described in the first aspect are implemented.

[0055] According to a fifth aspect of an embodiment 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 steps of the spectrum sharing method based on the new networking of aggregation stations and AI base stations as described in the first aspect are implemented.

[0056] According to a sixth aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps of the spectrum sharing method based on the new networking of the aggregation station and the AI ​​base station described in the first aspect are implemented.

[0057] The present disclosure can fully utilize the elevated wireless backhaul capabilities of the aggregation station and the edge computing and flexible deployment characteristics of the AI ​​base station to build an efficient and reliable network system through the reasonable design of the networking architecture. Through the periodically updated radio electromagnetic map, this method can reflect the electromagnetic conditions in the network environment in real time, including key information such as signal strength and interference level. When determining the resource allocation plan for the current time slot, this method aims to maximize the minimum terminal capacity in the networking architecture, which can ensure that the terminals in the entire network can obtain relatively balanced service quality, which helps to improve the fairness and overall performance of the network. An anti-interference plan is formulated based on the resource allocation plan for the current time slot, which can be flexibly adjusted according to specific network environments and terminal needs. Compared with traditional satellite networks and terrestrial networks, this method achieves higher cost-effectiveness and more convenient maintenance through the networking of aggregation stations and AI base stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0059] Figure 1 1 is a schematic diagram of a network architecture based on a convergence station and an AI base station according to an embodiment of the present disclosure;

[0060] Figure 2 1 is a schematic diagram of the steps of a spectrum sharing method based on a new type of networking of a convergence station and an AI base station, as shown in an embodiment of the present disclosure;

[0061] Figure 3 This is a networking architecture based on a convergence station and an AI base station in a certain time slot shown in an embodiment of the present disclosure;

[0062] Figure 4 1 is a schematic diagram of an AI base station position optimization for a certain time slot according to an embodiment of the present disclosure;

[0063] Figure 5 This is a block diagram of a novel networking and spectrum sharing device based on a convergence station and an AI base station, as shown in an embodiment of the present disclosure;

[0064] Figure 6 is a schematic diagram of an electronic device shown in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0066] The terms "first", "second", etc. in the specification and claims of the present disclosure are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present disclosure can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects related to each other are in an "or" relationship.

[0067] Aggregation stations are relatively high, new types of elevated wireless backhaul sites. AI base stations are new types of base stations with edge computing capabilities that can be deployed flexibly and flexibly. AI base station networking transcends the constraints of power, fiber, and tower infrastructure, enabling flexible, on-demand deployment across multiple locations and platforms. For example, during natural disasters such as earthquakes and fires, mobile AI base stations can be flexibly deployed on demand in areas beyond the reach of ground-based mobile base stations, enabling emergency communications.

[0068] Comprehensively comparing coverage, survivability, latency, spectrum efficiency, and power efficiency, aggregation stations are more cost-effective and easier to maintain than traditional satellite networks. AI base station networking is more flexible than terrestrial networks and can be deployed on-demand across multiple regions and platforms.

[0069] However, there are still many technical problems to be solved in the networking of aggregation stations and AI base stations. The most serious problem is that while spectrum sharing based on the networking of aggregation stations and AI base stations brings high spectrum reuse gain to network users, it also brings complex mixed co-channel interference to the network.

[0070] In order to solve the above technical problems, the present disclosure provides a spectrum sharing system based on a new type of networking of aggregation stations and AI base stations, including: multiple aggregation stations, which are used to provide services to AI base stations; multiple AI base stations, which are used to provide services to terminals; terminals covered by each AI base station can communicate through the AI ​​base station; a knowledge base, which is used to generate a corresponding radio electromagnetic map based on the information of each device in the networking architecture, and a network controller, which is used to calculate the corresponding resource configuration plan based on the radio electromagnetic map.

[0071] This type of networking includes multiple aggregation stations and multiple AI base stations.

[0072] The aggregation station is an elevated wireless backhaul site that provides extensive wireless coverage. It uses phased array multi-beam antennas with beam-hopping capabilities. The aggregation station is responsible for transmitting signals from the AI ​​base station back to the core network, acting as a signal relay to ensure efficient data transmission.

[0073] AI base stations can be flexibly deployed across multiple locations and platforms based on demand, providing essential communication services, particularly in areas beyond the reach of traditional base stations. Equipped with edge computing units, AI base stations can locally process data and perform computing tasks, reducing data transmission latency, improving response speed, and adapting to the demands of real-time applications. AI base stations are responsible for providing access services to users, working in conjunction with aggregation stations to ensure stable signals and good communication quality.

[0074] The AI ​​base station exchanges information with the aggregation station through the backhaul link.

[0075] This network also includes a knowledge base and a network controller. The knowledge base is responsible for collecting various information about user terminals, including location, service priority, service type, and interference factors. The knowledge base can quickly process and update information, using real-time uploaded information to generate and update radio electromagnetic maps, ensuring real-time decision-making. Radio electromagnetic maps can reflect signal coverage and interference levels in different areas, providing a basis for optimizing spectrum resource allocation. The knowledge base can continuously optimize resource allocation and network management strategies to adapt to changing network needs. The network controller is responsible for formulating spectrum resource allocation plans and optimizing spectrum usage based on real-time data and models provided by the knowledge base to improve overall network performance and spectrum utilization. The network controller can dynamically schedule resources between aggregation stations and AI base stations based on user needs and network status, ensuring that users receive good service at all times and locations.

[0076] Figure 1 This is a schematic diagram of a network architecture based on a convergence station and an AI base station shown in an embodiment of the present disclosure. Figure 1 As shown, the networking architecture includes multiple aggregation stations and multiple AI base stations. The aggregation stations are used to provide services to the AI ​​base stations, and the AI ​​base stations are used to provide services to the terminals. The terminals covered by each AI base station can communicate through the AI ​​base station. The networking architecture also includes a knowledge base and a network controller. The knowledge base is used to generate a corresponding radio electromagnetic map based on the information of each device in the networking architecture, such as the first core factor, the second core factor and the terminal information. The network controller is used to calculate the corresponding resource allocation plan based on the radio electromagnetic map.

[0077] Using the embodiments of this disclosure, a new network based on aggregation stations and AI base stations can effectively mitigate interference during spectrum sharing and improve overall network performance and user experience through flexible deployment, efficient spectrum utilization, enhanced survivability, and intelligent interference management. This new network based on aggregation stations and AI base stations can achieve high spectrum reuse gain, improve spectrum utilization, and thus maximize overall network performance.

[0078] The present disclosure proposes a spectrum sharing method based on a new type of networking of aggregation stations and AI base stations. The method can efficiently allocate and manage wireless resources such as spectrum and power of the entire system, effectively solve the problem of mixed co-channel interference, and help to give full play to the advantages of networking based on aggregation stations and AI base stations, and further improve the overall performance of the network.

[0079] Figure 2 This is a schematic diagram of the steps of a spectrum sharing method based on a new type of networking of a convergence station and an AI base station shown in an embodiment of the present disclosure. Figure 1 As shown, the method may specifically include the following steps:

[0080] Step S11: Determine the networking architecture based on the aggregation station and the AI ​​base station.

[0081] An aggregation station is an elevated base station that provides wide-area coverage and stable wireless signal backhaul. The coverage radius of an aggregation station effectively connects multiple AI base stations. An AI base station is a flexible and mobile base station equipped with edge computing capabilities. The deployment flexibility of an AI base station enables rapid response in a variety of environments and enables efficient resource allocation and beam management with the aggregation station, thereby improving spectrum utilization and power efficiency. The AI ​​base station is responsible for providing wireless signal coverage to user terminals.

[0082] Determine the network coverage, number of terminals, data transmission rate, and other requirements to determine the basic parameters of the network architecture. Based on the requirements, select the appropriate wireless communication technology, such as 5G or Wi-Fi, and determine the connection method between the aggregation station and the AI ​​base station. This disclosure uses a backhaul link to connect the aggregation station and the AI ​​base station.

[0083] The networking architecture of the aggregation station and AI base stations includes the location of the aggregation station, the number and distribution of AI base stations, etc.

[0084] Step S12: Determine a networking system model based on a convergence station and an AI base station according to the networking architecture; the networking system model includes a periodically updated radio electromagnetic map.

[0085] Leveraging technologies such as spectrum sensing and geolocation information databases, we monitor and perceive the radio electromagnetic environment in real time, creating a radio electromagnetic map that reflects spectrum resource utilization. Based on dynamic changes in spectrum resources, we periodically update the radio electromagnetic map to ensure its accuracy and real-time performance. Based on the radio electromagnetic map, we construct a networking system model consisting of aggregation stations, AI base stations, and terminals. This networking system model can simulate data transmission and spectrum sharing within the network.

[0086] Step S13: Determine the resource allocation scheme for the current time slot based on the radio electromagnetic map included in the networking system model; the resource allocation scheme for the current time slot is determined with the goal of maximizing the minimum terminal capacity in the networking architecture; the terminal capacity represents: the ability of the terminal to transmit data.

[0087] Based on the network system model, analyze each terminal's data transmission capabilities, or terminal capacity. Maximize the minimum terminal capacity within the network architecture to ensure that each terminal in the network has sufficient resources. Develop a resource allocation plan for the current time slot based on the optimization goals and the radio electromagnetic map.

[0088] Step S14: According to the resource configuration plan, an anti-interference plan for reducing spectrum sharing interference in the current time slot is determined.

[0089] Based on the resource allocation plan and radio electromagnetic maps, analyze the potential sources and levels of interference during spectrum sharing. Develop appropriate anti-interference strategies based on these sources and levels. Real-time monitoring of network performance and spectrum utilization can be used to evaluate the effectiveness of anti-interference plans and make necessary adjustments and optimizations based on the results.

[0090] By adopting the embodiments of the present disclosure, by determining the system model according to the networking architecture and introducing a periodically updated radio electromagnetic map, the electromagnetic conditions in the network environment can be reflected in real time, providing data support for subsequent resource allocation and anti-interference strategies. The radio electromagnetic map is used to determine the resource allocation scheme for the current time slot, and the scheme is determined with the goal of maximizing the minimum terminal capacity in the networking architecture, which can ensure that terminals in the entire network can obtain relatively balanced service quality. The anti-interference scheme for reducing spectrum sharing interference in the current time slot is determined based on the resource allocation scheme, and can be flexibly adjusted according to specific network environments and terminal requirements, thereby effectively suppressing interference and improving spectrum utilization efficiency. Through the implementation of the anti-interference scheme, the method can reduce mixed co-channel interference in the network while ensuring high spectrum reuse.

[0091] In an optional embodiment, the networking architecture includes a knowledge base, and the networking system model based on the aggregation station and the AI ​​base station is determined according to the networking architecture, including: determining the first coverage radius of the aggregation station according to the phased array multi-beam antenna adopted by the aggregation station in the networking architecture; the networking system model determines the first core factor of the aggregation station contained in the first coverage radius around the first coverage radius; determining the second coverage radius of the AI ​​base station according to the mobile antenna adopted by the AI ​​base station in the networking architecture; the networking system model determines the second core factor of the AI ​​base station contained in the second coverage radius around the second coverage radius; wherein, each terminal covered by the AI ​​base station uploads its corresponding terminal information to the knowledge base through the AI ​​base station; the knowledge base determines the radio electromagnetic map corresponding to the networking system model through the terminal information, the first core factor and the second core factor.

[0092] The actual coverage range of the phased array multi-beam antenna of the aggregation station can be measured and calibrated to ensure the accuracy of the measurement results. The measurement results can be corrected according to the antenna's transmission power, beam parameters such as beam width and beam pointing, as well as environmental factors such as atmospheric attenuation and terrain influence.

[0093] Based on the calibrated measurement results, the first coverage radius R of the aggregation station in the networking system model is determined. The first coverage radius R can cover the expected service area of ​​the aggregation station, and the determination of the first coverage radius takes into account a certain degree of redundancy to cope with factors such as environmental changes and equipment aging.

[0094] Collect data on the first core factors of the aggregation station, such as its latitude and longitude, altitude, beam parameters, and transmit power. Beam parameters include beam width, beam pointing, and number of beams. Organize and analyze each first core parameter to ensure the accuracy and completeness of the first core parameters for each aggregation station. Determine the collected first core parameters for each aggregation station as the first core parameters for each aggregation station in the networking system model.

[0095] AI base stations use mobile antennas. Their coverage is measured based on their characteristics, such as antenna gain and beam pointing capability. Because AI base stations are deployed in a mobile manner, their coverage may vary with their location. Therefore, the coverage measurement results can be dynamically adjusted.

[0096] Based on the measurement results of the AI ​​base station coverage range, the second coverage radius r of each AI base station in the networking system model is determined. The second coverage radius r of the AI ​​base station can cover the expected edge service area, and the second coverage radius r also takes into account the mobility and flexibility of the AI ​​base station.

[0097] Collect data on each AI base station's second core factors, such as its trajectory, beam parameters, and transmit power. Beam parameters include beam width and beam pointing. The second core factors of each AI base station must be updated and analyzed in real time to reflect dynamic changes. The collected second core factors of each AI base station are used as the corresponding second core parameters for each AI base station in the networking system model.

[0098] Terminals covered by each AI base station upload their corresponding terminal information to the knowledge base through the AI ​​base station. Terminal information includes location information, service priority, service type, interference factors, etc. When collecting and uploading terminal information, each terminal uses its own configured environmental information collection unit to achieve this.

[0099] The knowledge base performs comprehensive analysis and calculations based on received terminal information, the first core factor of the aggregation station, and the second core factor of the AI ​​base station. This calculation also considers channel models, such as the three-state model based on Markov chains, and signal fading characteristics, such as free space path loss and shadow fading, to generate a radio electromagnetic map corresponding to the network system model. The radio electromagnetic map should reflect information such as the electromagnetic environment, signal strength, and interference within the network.

[0100] By adopting the embodiments of the present disclosure, the networking system model corresponding to the networking architecture is determined by collecting and analyzing environmental and business information in real time, thereby updating the radio electromagnetic map in real time, which helps the network controller to formulate spectrum resource allocation plans more accurately, thereby improving the utilization efficiency of spectrum resources. The collaborative work of the aggregation station and the AI ​​base station can achieve wide coverage of different areas, and adapt to environmental changes by dynamically adjusting parameters such as antenna beams and transmission power, thereby enhancing the reliability and stability of the network. The AI ​​base station has the ability to be deployed flexibly and can be flexibly adjusted according to business needs and network conditions, affecting the first core factor of the aggregation station and the second core factor of the AI ​​base station, and the networking system model can support the consideration of multiple core factors, providing a basis for network expansion and upgrade.

[0101] Among them, in an optional embodiment, it also includes: the networking architecture based on the aggregation station and the AI ​​base station adopts directional antenna communication to achieve wide-area coverage without interfering with the satellite network; by adopting directional antenna communication to achieve wide-area coverage, the spectrum utilization and power efficiency are improved.

[0102] When designing and implementing a networking architecture based on aggregation stations and AI base stations, it is necessary to ensure that no interference is caused to existing satellite network communications, while utilizing directional antenna communication technology to achieve wider network coverage.

[0103] In order to ensure that the networking architecture does not interfere with existing satellite network communications, it is necessary to consider compatibility with existing satellite networks and follow relevant interference standards and regulations when selecting parameters such as frequency, beam direction and power to ensure that the new network architecture does not have a negative impact on satellite communications.

[0104] Directional antennas can focus signals in a specific direction rather than radiating them in all directions. This allows signals to be transmitted more efficiently to the target receiver while reducing interference in other directions. This allows spectrum resources in the same frequency band to be more effectively utilized, thereby improving spectrum efficiency. A networking architecture based on aggregation stations and AI base stations, built using directional antennas, can achieve wider coverage while ensuring signal strength and quality, effectively serving more users while reducing interference.

[0105] By adopting the embodiments of the present disclosure, a networking architecture based on aggregation stations and AI base stations is deployed without interfering with the satellite network, which can protect the stability and reliability of satellite communications and ensure the normal operation of key services. Directional antennas can concentrate signals in a specific direction, reduce co-channel interference, and thus enable spectrum resources in the same frequency band to be shared by multiple users or devices, thereby improving the overall utilization of the spectrum. Through directional antenna technology, strong signal coverage can be provided in a wider area, and signal attenuation caused by obstacles can be effectively overcome. Directional antenna technology can achieve longer transmission distances and better signal quality at lower transmission power.

[0106] Among them, in an optional embodiment, the new networking includes: the networking architecture corresponding to the new networking supports 4G / 5G collaborative deployment; through the 4G / 5G collaborative deployment of the networking architecture, the terminal can seamlessly switch between the existing base station and the AI ​​base station in the networking architecture.

[0107] The networking architecture supports 4G / 5G coordinated deployment, allowing users to access services under different network conditions and leveraging the respective strengths of 4G and 5G. This is because existing mobile phones may only support 4G networks or both. Through 4G / 5G coordinated deployment, various devices can receive good service.

[0108] Seamless switching means that users can automatically switch between existing base stations and AI base stations while on the move without interrupting current communications or data transmission. This seamless switching is automatic, requiring no manual operation from the user, allowing for good connection quality and communication experience in different network environments. For example, in some areas, 4G networks may have better coverage, while in other areas, 5G networks may offer higher speeds and lower latency. Supporting seamless switching allows users to enjoy the best service under varying network conditions.

[0109] By implementing the embodiments of this disclosure, the coordinated deployment of 4G and 5G base stations can cover a wider area, enabling existing mobile phones to obtain stable network connections. Based on the user's mobility and network conditions, the optimal base station can be dynamically selected through seamless switching, ensuring that users receive good network service in different environments.

[0110] In an optional embodiment, the networking architecture includes a network controller, and the resource configuration scheme of the current time slot is determined according to the radio electromagnetic map included in the networking system model, including: moving the position of the AI ​​base station in the networking system model; obtaining the real-time updated radio electromagnetic map according to the real-time movement of the AI ​​base station in the networking system model; calculating the terminal capacity of each terminal through the network controller in combination with the real-time updated radio electromagnetic map; determining the terminal with the smallest terminal capacity among the terminals as the target terminal according to the terminal fairness strategy; updating the target terminal in real time according to the movement of the AI ​​base station, and determining the target position of each AI base station in the current time slot when the terminal capacity of the target terminal reaches the maximum value; and determining the resource configuration scheme of the current time slot according to the target position of the AI ​​base station in the current time slot.

[0111] The networking architecture also includes a network controller, which can calculate the resource allocation plan corresponding to the radio electromagnetic map based on the radio electromagnetic map of the current time slot.

[0112] In the networking system model, the initial positions of the AI ​​base stations in the time slot are determined. These positions can be preset, based on historical data, or dynamically adjusted according to current needs.

[0113] Get the radio electromagnetic map of the current time slot. The radio electromagnetic map of the current time slot reflects the electromagnetic conditions in the network environment.

[0114] Move the AI ​​base station in the networking system model. As the AI ​​base station moves in the networking system model, the radio electromagnetic map is updated in real time to reflect the latest electromagnetic environment.

[0115] The network controller uses algorithms or models based on the real-time updated radio electromagnetic map to calculate the terminal capacity of each terminal. The calculated terminal capacity reflects the terminal's ability to transmit data in the current electromagnetic environment.

[0116] Applying a terminal fairness policy ensures that all terminals in the network receive relatively fair service. The core of this policy is to focus on the terminal with the smallest capacity in the Zuwang architecture and maximize its corresponding capacity, ensuring that the capacity of all remaining terminals can meet demand.

[0117] Using the terminal fairness strategy, the terminal with the smallest capacity among all terminals is determined as the target terminal. The target terminal is updated as the AI ​​base station moves. Specifically, if a new terminal with a smaller capacity than the current target terminal appears during the AI ​​base station's movement, the target terminal is updated to the new terminal with the smallest capacity.

[0118] With the goal of maximizing the terminal capacity of the target terminal, the location of the AI ​​base station is adjusted using an algorithm or optimization model. Multiple iterations are performed until the AI ​​base station location that maximizes the target terminal capacity is found. This location is then determined as the target location for the AI ​​base station in the current time slot. The target location of the AI ​​base station means that the AI ​​base station will be located at this target location to provide service to the terminal in that time slot.

[0119] Based on the target location of the AI ​​base station in the current time slot and combined with other information in the network controller, such as service requirements and spectrum resources, the resource configuration plan for the current time slot is determined. The resource configuration plan can include multiple aspects such as spectrum allocation, power control, and access strategy.

[0120] Implement the determined resource allocation plan into the network architecture to optimize network performance and user experience. After the resource allocation plan is implemented into the network architecture, network performance can be monitored in real time, including indicators such as terminal capacity, spectrum utilization efficiency, and network latency. Based on the monitoring results, the resource allocation plan can be adjusted and optimized as necessary.

[0121] By adopting the embodiments of the present disclosure, the dynamic changes of the network environment can be reflected by updating the radio electromagnetic map in real time. The terminal capacity of each terminal is calculated by the network controller, and the terminal with the smallest terminal capacity is selected as the target terminal based on the terminal fairness policy, which can ensure that all terminals can obtain the fairest and most optimized resource allocation possible, which helps to improve the throughput and user experience of the entire network. The target terminal is updated in real time according to the movement of the AI ​​base station, and the target position of the AI ​​base station is determined when the terminal capacity of the target terminal reaches the maximum value, realizing the intelligent positioning of the AI ​​base station, which not only improves the utilization rate of network resources, but also reduces the cost of network construction because the AI ​​base station can be deployed more flexibly. The system can dynamically adjust the resource allocation plan to adapt to the ever-changing network environment. This helps to improve the efficiency of resource allocation and ensure that network resources are optimally utilized.

[0122] In an optional embodiment, the capacity of each terminal is calculated by the network controller in combination with the radio electromagnetic map updated in real time, including: determining, based on the radio electromagnetic map, a first signal-to-noise ratio corresponding to each of the aggregation stations, and determining a second signal-to-noise ratio corresponding to each of the AI ​​base stations; determining, based on each of the first signal-to-noise ratios, a first total throughput of the communication link from the aggregation station to the AI ​​base station; the first total throughput represents: the data transmission capacity of the communication link from each of the aggregation stations to each of the AI ​​base stations; determining, based on each of the second signal-to-noise ratios, a second total throughput of the communication link from the AI ​​base station to the terminal; the second total throughput represents: the data transmission capacity of the communication link from each of the AI ​​base stations to each of the terminals; determining the number of terminals included in the networking architecture; determining a first capacity based on the first total throughput and the number of terminals; determining a second capacity from the AI ​​base station to each of the terminals based on the second total throughput; and determining the smaller value of the first capacity and the second capacity as the terminal capacity of each of the terminals.

[0123] From the real-time updated radio electromagnetic map, the location information of each aggregation station and AI base station as well as the corresponding parameters such as signal strength and interference level are extracted.

[0124] The first signal-to-noise ratio and the second signal-to-noise ratio may be a signal-to-interference plus noise ratio (SINR). SINR is an important indicator for measuring receiver performance. By measuring and calculating SINR, the performance of a wireless communication system can be evaluated and used for network optimization and resource allocation.

[0125] Based on the signal strength and interference level between the aggregation station and the AI ​​base station in the radio electromagnetic map, the first signal-to-noise ratio of the communication link from each aggregation station to each AI base station in the current time slot is calculated. Each first signal-to-noise ratio reflects the signal quality of the communication link from the aggregation station to the AI ​​base station.

[0126] The following is a specific calculation formula for the first signal-to-noise ratio of the AI ​​base station u under the aggregation station q at time slot j. The first signal-to-noise ratio can be used express:

[0127]

[0128] in is the antenna gain of the aggregation station to the AI ​​base station, Indicates the antenna gain of the AI ​​base station to the aggregation station, is the base station transmission power corresponding to AI base station u at time slot j under aggregation station q, It represents the channel parameter of the AI ​​base station u receiving the signal from the base station. This parameter is related to the location of the transceiver and is given by the wireless environment map generated in the knowledge base. is the transmission power of the aggregation station b in time slot j, represents the channel parameters when AI base station u receives the interference signal from aggregation station b in time slot j, K is the Boltzmann constant, is the noise temperature at the receiving end, The available bandwidth of AI base station user u is The value of is zero. is the number of beams, is the interference factor between the aggregation station b and the aggregation station q. Since the interference gain is caused by the side lobe, its magnitude is much smaller than the main lobe gain, and it is also related to visibility, beam frequency, and bandwidth overlap.

[0129] Based on the signal strength and interference level between the AI ​​base station and the terminal in the radio electromagnetic map, the second signal-to-noise ratio of the communication link from each AI base station to each terminal is calculated. Each second signal-to-noise ratio reflects the signal quality of the communication link from the AI ​​base station to the terminal.

[0130] The following is a specific calculation formula for the second signal-to-noise ratio of user n in time slot j under the AI ​​base station. The second signal-to-noise ratio can be used express:

[0131]

[0132] in, is the antenna gain of the AI ​​base station to the user, Indicates the antenna gain of the user to the AI ​​base station, and Far below and . is the transmit power of AI base station u corresponding to user n in time slot j, Indicates that user n receives channel parameters from AI base station u. and represents the transmission power and channel parameters of the interference signal received by user n from AI base station q in time slot j. is the bandwidth of user n. Refers to the inter-beam interference factor related to the inter-beam interference coefficient.

[0133] Based on the first signal-to-noise ratios from each aggregation station to each AI base station, a first total throughput of the communication link from the aggregation station to the AI ​​base station is determined, which represents the data transmission capacity from each aggregation station to each AI base station.

[0134] The following is a specific calculation formula for the first total throughput. The first total throughput can be calculated using To express:

[0135]

[0136] in, is the channel allocation factor from the aggregation station to the AI ​​base station, is the number of AI base stations q under the aggregation station.

[0137] Based on the second signal-to-noise ratio from each AI base station to each terminal, a second total throughput of the communication link from the AI ​​base station to the terminal is calculated. The second total throughput represents the data transmission capability of each AI base station to each terminal.

[0138] The following is a specific calculation formula for the second total throughput. The second total throughput can be calculated using To express:

[0139]

[0140] in, is the channel allocation factor from the AI ​​base station to the terminal.

[0141] It is the total capacity of the network architecture. and It conforms to the 0-1 binomial distribution.

[0142] Count the number of currently active terminals in the networking architecture and calculate the terminal capacity corresponding to each terminal. The capacity of any terminal can be determined by comparing the capacity of the single link from the AI ​​base station to the terminal, that is, the second capacity, and the average capacity of the first total throughput for all terminals, that is, the first capacity, and selecting the smaller of the two capacities as the capacity of the terminal. The reason for comparing the capacity of the single link from the AI ​​base station to the terminal and the average capacity of the first total throughput for all terminals is that the total capacity from the AI ​​base station to the terminal is affected by the capacity from the aggregation station to the AI ​​base station, and the capacity of the terminal is limited by the bottleneck link in the network, that is, the part with smaller capacity. Therefore, in the scenario of improving communication quality by optimizing the location of the AI ​​base station, the smaller value of the first capacity and the second capacity is determined as the terminal capacity of the terminal.

[0143] Based on this, the objective function of the AI ​​base station location optimization problem can be mathematically modeled as shown in the formula:

[0144]

[0145] Through the mathematical modeling formula corresponding to the objective function, the target terminal with the smallest terminal capacity among all terminals in the networking architecture and the terminal capacity corresponding to the target terminal can be determined during the movement of the AI ​​base station. When the result of the mathematical modeling formula is maximized, the terminal capacity of the target terminal is maximized.

[0146] Using the embodiments of the present disclosure, the signal-to-noise ratio is an important indicator for measuring the quality of the communication link, which directly affects the reliability and throughput of data transmission. Therefore, the first total throughput and the second total throughput calculated based on the accurate signal-to-noise ratio can more truly reflect the actual data transmission capacity of the network. Calculations are performed based on a real-time updated radio electromagnetic map, so it is possible to quickly respond to changes in the network environment. When new interference sources appear in the network, terminals move, or network topology changes, the calculation parameters and results can be adjusted in time to ensure the stability and optimization of network performance. By calculating the capacity of each terminal, the network controller can understand the data transmission needs and capabilities of each area or node in the network, which helps network operators to reasonably allocate network resources according to actual needs to improve the overall performance of the network and user experience.

[0147] Figure 3 This is a networking architecture based on a convergence station and an AI base station in a certain time slot shown in an embodiment of the present disclosure. Figure 3 As shown, the obstacle information in the networking architecture is determined, and the aggregation station communicating with the AI ​​base station is adjusted so that there are no obstacles blocking the communication link from the aggregation station to the AI ​​base station.

[0148] Figure 4 This is a schematic diagram of an AI base station location optimization for a certain time slot shown in an embodiment of the present disclosure. Figure 4 As shown in Figure 2, the position of each AI base station in the network architecture is determined in this time slot through terminal fair measurement. The position of the AI ​​base station and the aggregation station is Figure 4 It is specifically represented by the horizontal and vertical coordinates.

[0149] After determining the position of the AI ​​base station in the networking architecture at a certain time slot, the anti-interference solution used to reduce spectrum sharing interference at that time slot is then determined.

[0150] Among them, in an optional embodiment, the resource configuration scheme includes the physical location of the AI ​​base station in the current time slot; in the case of spectrum sharing in combination with a satellite network, according to the resource configuration scheme, an anti-interference scheme for reducing spectrum sharing interference in the current time slot is determined, including: determining the interference type of spectrum sharing interference; the interference type includes intra-system interference and inter-system interference; the intra-system interference characterization: the interference between the aggregation station and the AI ​​base station in the networking architecture; the inter-system interference characterization: the interference between the networking architecture and the satellite network; determining the physical location of the AI ​​base station in the current time slot in the resource configuration scheme, and determining the antenna radiation pattern of the aggregation station in the current time slot; adjusting the aggregation station communicating with the AI ​​base station according to the geographical location relationship between the aggregation station and the AI ​​base station; reducing intra-system interference by adjusting the aggregation station communicating with the AI ​​base station; predicting the timing when the inter-system interference exceeds the interference threshold according to the geographical location information and the resource configuration scheme; reducing inter-system interference through interference threshold constraints.

[0151] In the case of applying the networking architecture based on aggregation stations and AI base stations proposed in this disclosure, it is usually combined with existing traditional base stations. The aggregation stations, AI base stations and traditional base stations conduct deep information interaction through the backhaul link to realize spectrum sharing between the networking architecture and the traditional network. In the process of spectrum sharing between the networking architecture and the traditional network, there will be two types of interference, including intra-system interference and inter-system interference. Intra-system interference means that in the process of spectrum sharing, there is intra-system interference between the aggregation stations in the networking architecture and the AI ​​base stations; inter-system interference means that in the process of spectrum sharing, there will be a certain degree of interference between the networking architecture and the traditional network, such as the satellite network.

[0152] The resource allocation plan determines the physical location of the AI ​​base station in the current time slot and the antenna pattern. The antenna pattern reflects the communication direction between the aggregation station and the AI ​​base station. Based on the antenna pattern, the signal transmission path and possible interference between the aggregation station and the AI ​​base station are analyzed. The antenna pattern data of the aggregation station includes parameters such as antenna gain and beamwidth.

[0153] Based on the geographical location of the AI ​​base station and the aggregation station, analyze the causes and impact of interference within the system. Based on the interference analysis results, formulate an adjustment strategy for the aggregation station, including adjustments to antenna direction and transmit power. Implement the adjustment strategy, and monitor the signal transmission quality and interference situation in real time after the adjustments. Specifically, the aggregation station communicating with the AI ​​base station can be appropriately adjusted. For example, if interference is high when the AI ​​base station communicates with aggregation station 1, adjust the AI ​​base station to communicate with aggregation station 2 to improve communication quality from the aggregation station to the AI ​​base station. This reduces interference within the system.

[0154] Leveraging geographic location information and resource allocation plans, combined with historical data or prediction models, we predict when inter-system interference will exceed the threshold. We analyze the prediction results to identify the source of interference and its impact. We can adjust the satellite network's operating frequency and optimize the network architecture. After implementing inter-system interference mitigation measures, we can continuously monitor the interference situation to ensure it remains within acceptable limits.

[0155] By adopting the embodiments of the present disclosure, effective control of intra-system interference and inter-system interference is achieved by comprehensively considering the physical location of the AI ​​base station, the antenna pattern of the aggregation station, and the spectrum sharing of the satellite network. By determining the physical location of the AI ​​base station in the current time slot, and the antenna pattern of the aggregation station, the geographical location relationship between the base station and the aggregation station can be accurately grasped. According to the geographical location relationship, the aggregation station communicating with the AI ​​base station is adjusted, thereby effectively avoiding or reducing the interference between the base station and the aggregation station. The adjustment can be made dynamically based on real-time data to ensure that the intra-system interference is always kept at a low level. By predicting the time when the inter-system interference exceeds the interference threshold and taking corresponding interference threshold constraint measures, the impact of inter-system interference can be significantly reduced. Analysis and optimization can be performed based on historical data and real-time data to ensure that the inter-system interference is effectively controlled.

[0156] Among them, in an optional embodiment, adjusting the aggregation station that communicates with the AI ​​base station based on the geographical location relationship between the aggregation station and the AI ​​base station includes: determining the geographical location relationship between each of the AI ​​base stations and each of the aggregation stations; the geographical location relationship includes obstacle information; determining the aggregation station without obstacles between the AI ​​base station and the AI ​​base station as the first aggregation station, and adjusting the AI ​​base station to communicate with the first aggregation station; in the case where there are multiple first aggregation stations, determining the first aggregation station that is closest to the AI ​​base station among the multiple first aggregation stations as the second aggregation station, and adjusting the AI ​​base station to communicate with the second aggregation station.

[0157] Determine the precise geographic location information of all AI base stations and aggregation stations, including longitude, latitude, altitude, etc.; and update terrain data, including information on buildings, mountains, trees, etc. that may pose communication obstacles.

[0158] Based on the collected geographic location information, the straight-line distance between each AI base station and each aggregation station is calculated. Using topographic data, the obstacle information between the AI ​​base station and the aggregation station is analyzed and determined, including the type, height, and location of the obstacle.

[0159] For each AI base station, select the aggregation station with no obstacles between it and the aggregation station, and determine the aggregation station selected at this time as the first aggregation station. The antenna pattern is used to determine whether each AI base station is communicating with the first aggregation station. If not, the AI ​​base station is adjusted to communicate with the selected first aggregation station so that there are no obstacles between the AI ​​base station and the aggregation station in communication. This is because obstacle loss can cause the signal energy to be significantly attenuated by more than 20dB. Among them, if there are obstacles between an AI base station and all aggregation stations, consider using higher-frequency electromagnetic waves, such as millimeter waves, or adopting other technical means, such as relay stations, to overcome the obstacles.

[0160] If there are multiple first aggregation stations, the actual distance between each first aggregation station and the AI ​​base station is calculated, and the first aggregation station with the closest actual distance is determined as the second aggregation station. If the distances between multiple first aggregation stations and the AI ​​base station are very close, for example, within the error range, the distances between each first aggregation station and the AI ​​base station are equal, then other factors such as communication quality and network load can be considered to make the final selection.

[0161] According to the determined second aggregation station, adjust the communication link between the AI ​​base station and the second aggregation station so that the AI ​​base station can communicate smoothly with the second aggregation station. During the communication between the AI ​​base station and the second aggregation station, monitor the quality of the communication link and adjust and optimize it as needed.

[0162] By adopting the embodiment of the present disclosure, by screening out the aggregation station with no obstacles in communication with the AI ​​base station as the first aggregation station, signal attenuation and communication interruption caused by obstacles are effectively avoided, thereby optimizing the communication link between the AI ​​base station and the aggregation station. After determining the first aggregation station, if there are multiple first aggregation stations, the first aggregation station closest to the AI ​​base station is further selected as the second aggregation station, and the AI ​​base station is adjusted to communicate with the second aggregation station. This not only reduces the signal transmission distance and path loss, but also helps to reduce multipath interference caused by reflection, refraction, etc. during signal transmission, thereby further reducing interference within the system. By fine-tuning the communication relationship between the AI ​​base station and the aggregation station, spectrum resources can be used more effectively. In the scenario of spectrum sharing, reducing interference within the system means that limited spectrum resources can be used more efficiently, thereby supporting more users and devices to communicate and improving the capacity and performance of the overall network.

[0163] Figure 5 This is a block diagram of a spectrum sharing device based on a new type of network of aggregation stations and AI base stations shown in an embodiment of the present disclosure. Figure 5 As shown, the device includes:

[0164] A network architecture determination module 510 is configured to determine a network architecture based on a hub and an AI base station;

[0165] A networking system model determination module 520 is configured to determine a networking system model based on a hub and an AI base station according to the networking architecture; the networking system model includes a periodically updated radio electromagnetic map;

[0166] The resource allocation scheme determining module 530 is configured to determine a resource allocation scheme for a current time slot based on the radio electromagnetic map included in the networking system model; the resource allocation scheme for the current time slot is determined with the goal of maximizing the minimum terminal capacity in the networking architecture; the terminal capacity represents the ability of a terminal to transmit data;

[0167] The anti-interference scheme determining module 540 is configured to determine an anti-interference scheme for reducing spectrum sharing interference in a current time slot according to the resource configuration scheme.

[0168] Optionally, the networking architecture includes a network controller, and the resource configuration scheme determination module is specifically configured to execute:

[0169] Moving the position of the AI ​​base station in the networking system model;

[0170] Obtaining the radio electromagnetic map updated in real time according to the real-time movement of the AI ​​base station in the networking system model;

[0171] Calculating the terminal capacity of each terminal by combining the wireless electromagnetic map updated in real time with the network controller;

[0172] According to the terminal fairness policy, determining the terminal with the smallest terminal capacity among the terminals as the target terminal;

[0173] updating the target terminal in real time according to the movement of the AI ​​base station, and determining the target position of each AI base station in the current time slot when the terminal capacity of the target terminal reaches a maximum value;

[0174] A resource configuration scheme for the current time slot is determined according to the target position of the AI ​​base station in the current time slot.

[0175] Optionally, the resource configuration scheme determination module is specifically configured to execute:

[0176] Determining, based on the radio electromagnetic map, a first signal-to-noise ratio corresponding to each of the aggregation stations, and determining a second signal-to-noise ratio corresponding to each of the AI ​​base stations;

[0177] Determining a first total throughput of the communication link from the aggregation station to the AI ​​base station based on each of the first signal-to-noise ratios; wherein the first total throughput represents: a data transmission capacity of the communication link from each of the aggregation stations to each of the AI ​​base stations;

[0178] Determining a second total throughput of the communication link from the AI ​​base station to the terminal based on each of the second signal-to-noise ratios; wherein the second total throughput represents: a data transmission capacity of the communication link from each of the AI ​​base stations to each of the terminals;

[0179] Determining the number of terminals included in the networking architecture;

[0180] determining a first capacity according to the first total throughput and the number of terminals;

[0181] Determining, according to the second total throughput, a second capacity of the AI ​​base station to each of the terminals;

[0182] A smaller value between the first capacity and the second capacity is determined as the terminal capacity of each of the terminals.

[0183] Optionally, the resource configuration scheme includes the physical location of the AI ​​base station in the current time slot; in the case of spectrum sharing in conjunction with a satellite network, the anti-interference scheme determination module is specifically configured to execute:

[0184] Determine the interference type of spectrum sharing interference; the interference type includes intra-system interference and inter-system interference; the intra-system interference is characterized by: interference between the aggregation station and the AI ​​base station in the networking architecture; the inter-system interference is characterized by: interference between the networking architecture and the satellite network;

[0185] Determining the physical location of the AI ​​base station in the current time slot in the resource configuration scheme, and determining the antenna pattern of the aggregation station in the current time slot;

[0186] Adjusting the aggregation station communicating with the AI ​​base station based on the geographical location relationship between the aggregation station and the AI ​​base station; reducing interference within the system by adjusting the aggregation station communicating with the AI ​​base station;

[0187] According to the geographical location information and the resource allocation plan, the timing when the inter-system interference exceeds the interference threshold is predicted; and the inter-system interference is reduced through interference threshold constraints.

[0188] Optionally, the anti-interference scheme determination module is specifically configured to execute:

[0189] Determining a geographical location relationship between each of the AI ​​base stations and each of the aggregation stations; the geographical location relationship includes obstacle information;

[0190] Determine the aggregation station that has no obstacles between it and the AI ​​base station as the first aggregation station, and adjust the AI ​​base station to communicate with the first aggregation station;

[0191] In the case that there are multiple first aggregation stations, the first aggregation station closest to the AI ​​base station among the multiple first aggregation stations is determined as the second aggregation station, and the AI ​​base station is adjusted to communicate with the second aggregation station.

[0192] Optionally, the networking architecture includes a knowledge base, and the networking system model determination module is specifically configured to execute:

[0193] Determining a first coverage radius of the aggregation station based on a phased array multi-beam antenna used by the aggregation station in the networking architecture; determining, based on the first coverage radius, a first core factor of the aggregation station included in the first coverage radius by the networking system model;

[0194] Determining a second coverage radius of the AI ​​base station based on a mobile antenna used by the AI ​​base station in the networking architecture; determining, based on the second coverage radius, a second core factor of the AI ​​base station included in the second coverage radius by the networking system model;

[0195] Among them, the terminals covered by each AI base station upload their corresponding terminal information to the knowledge base through the AI ​​base station; the knowledge base determines the radio electromagnetic map corresponding to the networking system model through the terminal information, the first core factor and the second core factor.

[0196] Optionally, the device further comprises:

[0197] The coverage module is used to provide wide-area coverage by adopting directional antenna communication without interfering with the satellite network; by adopting directional antenna communication to provide wide-area coverage, spectrum utilization and power efficiency are improved.

[0198] Optionally, the network architecture determination module is specifically configured to execute:

[0199] The networking architecture supports 4G / 5G collaborative deployment;

[0200] Through the 4G / 5G coordinated deployment of the networking architecture, the terminal can seamlessly switch between the existing base station and the AI ​​base station in the networking architecture.

[0201] The present disclosure also provides an electronic device, Figure 6 , Figure 6 FIG. 1 is a schematic diagram of an electronic device shown in an embodiment of the present disclosure. Figure 6As shown, the electronic device 600 includes: a memory 610 and a processor 620. The memory 610 and the processor 620 are connected via a bus communication. A computer program is stored in the memory 610, and the computer program can be run on the processor 620, thereby implementing the steps in the spectrum sharing method based on the new networking of the aggregation station and the AI ​​base station disclosed in the embodiment of the present disclosure.

[0202] An embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the spectrum sharing method based on the new networking of aggregation stations and AI base stations as disclosed in the embodiment of the present disclosure are implemented.

[0203] The embodiments of the present disclosure also provide a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps in the spectrum sharing method based on the new networking of the aggregation station and the AI ​​base station as disclosed in the embodiments of the present disclosure.

[0204] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0205] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, apparatuses, or computer program products. Thus, the embodiments of the present disclosure may take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0206] The embodiments of the present disclosure are described with reference to the flowcharts and / or block diagrams of the methods, apparatuses, electronic devices, and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0207] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0208] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0209] Although some embodiments of the present disclosure have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the present disclosure.

[0210] The above is a detailed introduction to the spectrum sharing method and system for a new type of networking based on a convergence station and an AI base station provided by the present disclosure. Specific examples are used in this article to illustrate the principles and implementation methods of the present disclosure. The description of the above embodiments is only used to help understand the method of the present disclosure and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present disclosure, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present disclosure.

Claims

1. A spectrum sharing method based on a new type of networking of a convergence station and an AI base station, characterized in that: include: Determine the networking architecture based on aggregation stations and AI base stations; Determining a networking system model based on a convergence station and an AI base station according to the networking architecture; the networking system model includes a periodically updated radio electromagnetic map; Determining a resource allocation scheme for a current time slot based on a radio electromagnetic map included in the networking system model; wherein the resource allocation scheme for the current time slot is determined with the goal of maximizing the minimum terminal capacity in the networking architecture; wherein the terminal capacity represents: the ability of a terminal to transmit data; Determining, based on the resource allocation plan, an anti-interference plan for reducing spectrum sharing interference in a current time slot; The networking architecture includes a network controller, and determining a resource configuration scheme for a current time slot based on a radio electromagnetic map included in the networking system model includes: Moving the position of the AI ​​base station in the networking system model; Obtaining the radio electromagnetic map updated in real time according to the real-time movement of the AI ​​base station in the networking system model; Calculating the terminal capacity of each terminal by combining the wireless electromagnetic map updated in real time with the network controller; According to the terminal fairness policy, determining the terminal with the smallest terminal capacity among the terminals as the target terminal; updating the target terminal in real time according to the movement of the AI ​​base station, and determining the target position of each AI base station in the current time slot when the terminal capacity of the target terminal reaches a maximum value; Determining a resource allocation scheme for the current time slot based on a target position of the AI ​​base station in the current time slot; The resource configuration plan includes a physical location of the AI ​​base station in the current time slot. When spectrum sharing is performed in conjunction with a satellite network, an anti-interference plan for reducing spectrum sharing interference in the current time slot is determined based on the resource configuration plan, including: Determine the interference type of spectrum sharing interference; the interference type includes intra-system interference and inter-system interference; the intra-system interference is characterized by: interference between the aggregation station and the AI ​​base station in the networking architecture; the inter-system interference is characterized by: interference between the networking architecture and the satellite network; Determining the physical location of the AI ​​base station in the current time slot in the resource configuration scheme, and determining the antenna pattern of the aggregation station in the current time slot; Adjusting the aggregation station communicating with the AI ​​base station based on the geographical location relationship between the aggregation station and the AI ​​base station; reducing interference within the system by adjusting the aggregation station communicating with the AI ​​base station; According to the geographical location information and the resource allocation plan, the timing when the inter-system interference exceeds the interference threshold is predicted; and the inter-system interference is reduced through interference threshold constraints.

2. The method according to claim 1, characterized in that The network controller calculates the capacity of each terminal by combining the radio electromagnetic map updated in real time, including: Determining, based on the radio electromagnetic map, a first signal-to-noise ratio corresponding to each of the aggregation stations, and determining a second signal-to-noise ratio corresponding to each of the AI ​​base stations; Determining a first total throughput of the communication link from the aggregation station to the AI ​​base station based on each of the first signal-to-noise ratios; wherein the first total throughput represents: a data transmission capacity of the communication link from each of the aggregation stations to each of the AI ​​base stations; Determining a second total throughput of the communication link from the AI ​​base station to the terminal based on each of the second signal-to-noise ratios; wherein the second total throughput represents: a data transmission capacity of the communication link from each of the AI ​​base stations to each of the terminals; Determining the number of terminals included in the networking architecture; determining a first capacity according to the first total throughput and the number of terminals; Determining, according to the second total throughput, a second capacity of the AI ​​base station to each of the terminals; A smaller value between the first capacity and the second capacity is determined as the terminal capacity of each of the terminals.

3. The method according to claim 1, characterized in that The adjusting the aggregation station communicating with the AI ​​base station according to the geographical location relationship between the aggregation station and the AI ​​base station includes: Determining a geographical location relationship between each of the AI ​​base stations and each of the aggregation stations; the geographical location relationship includes obstacle information; Determine the aggregation station that has no obstacles between it and the AI ​​base station as the first aggregation station, and adjust the AI ​​base station to communicate with the first aggregation station; In the case that there are multiple first aggregation stations, the first aggregation station closest to the AI ​​base station among the multiple first aggregation stations is determined as the second aggregation station, and the AI ​​base station is adjusted to communicate with the second aggregation station.

4. The method according to claim 1, wherein The networking architecture includes a knowledge base, and determining a networking system model based on a convergence station and an AI base station according to the networking architecture includes: Determining a first coverage radius of the aggregation station based on a phased array multi-beam antenna used by the aggregation station in the networking architecture; determining, based on the first coverage radius, a first core factor of the aggregation station included in the first coverage radius by the networking system model; Determining a second coverage radius of the AI ​​base station based on a mobile antenna used by the AI ​​base station in the networking architecture; determining, based on the second coverage radius, a second core factor of the AI ​​base station included in the second coverage radius by the networking system model; Among them, the terminals covered by each AI base station upload their corresponding terminal information to the knowledge base through the AI ​​base station; the knowledge base determines the radio electromagnetic map corresponding to the networking system model through the terminal information, the first core factor and the second core factor.

5. The method according to any one of claims 1 to 4, characterized in that Also includes: The networking architecture based on aggregation stations and AI base stations uses directional antenna communication to achieve wide-area coverage without interfering with the satellite network. By adopting directional antenna communication to achieve wide-area coverage, spectrum utilization and power efficiency are improved.

6. A spectrum sharing system based on a new type of networking of aggregation stations and AI base stations, characterized in that: The method applied to any one of claims 1 to 5, comprising: Multiple aggregation stations, each of which is used to provide services to the AI ​​base station; Multiple AI base stations are used to provide services to terminals. Terminals covered by each AI base station can communicate through the AI ​​base station. The knowledge base is used to generate corresponding radio electromagnetic maps based on the information of each device in the network architecture. The network controller is used to calculate the corresponding resource allocation plan based on the radio electromagnetic map.

7. The system according to claim 6, characterized in that Also includes: The network architecture corresponding to the spectrum sharing system supports 4G / 5G collaborative deployment; Through the 4G / 5G coordinated deployment of the networking architecture, the terminal can seamlessly switch between the existing base station and the AI ​​base station in the networking architecture.

8. A spectrum sharing device based on a new type of network of aggregation stations and AI base stations, characterized in that: include: The network architecture determination module is used to determine the network architecture based on the aggregation station and the AI ​​base station; A networking system model determination module, configured to determine a networking system model based on a convergence station and an AI base station according to the networking architecture; the networking system model includes a periodically updated radio electromagnetic map; a resource allocation scheme determining module, configured to determine a resource allocation scheme for a current time slot based on a radio electromagnetic map included in the networking system model; the resource allocation scheme for the current time slot is determined with the goal of maximizing the minimum terminal capacity in the networking architecture; the terminal capacity represents the ability of a terminal to transmit data; an anti-interference scheme determining module, configured to determine an anti-interference scheme for reducing spectrum sharing interference in a current time slot according to the resource allocation scheme; The resource configuration scheme determination module is specifically used to execute: Moving the position of the AI ​​base station in the networking system model; Obtaining the radio electromagnetic map updated in real time according to the real-time movement of the AI ​​base station in the networking system model; Calculating the terminal capacity of each terminal by combining the wireless electromagnetic map updated in real time with the network controller; According to the terminal fairness policy, determining the terminal with the smallest terminal capacity among the terminals as the target terminal; updating the target terminal in real time according to the movement of the AI ​​base station, and determining the target position of each AI base station in the current time slot when the terminal capacity of the target terminal reaches a maximum value; Determining a resource allocation scheme for the current time slot based on a target position of the AI ​​base station in the current time slot; The anti-interference scheme determination module is specifically used to execute: Determine the interference type of spectrum sharing interference; the interference type includes intra-system interference and inter-system interference; the intra-system interference is characterized by: interference between the aggregation station and the AI ​​base station in the networking architecture; the inter-system interference is characterized by: interference between the networking architecture and the satellite network; Determining the physical location of the AI ​​base station in the current time slot in the resource configuration scheme, and determining the antenna pattern of the aggregation station in the current time slot; Adjusting the aggregation station communicating with the AI ​​base station based on the geographical location relationship between the aggregation station and the AI ​​base station; reducing interference within the system by adjusting the aggregation station communicating with the AI ​​base station; predicting, based on the geographic location information and the resource allocation plan, when inter-system interference exceeds an interference threshold; Reduce inter-system interference through interference threshold constraints.

Images