Novel networking frequency spectrum sharing method and system based on convergence station and AI base station

Abstract

The invention provides a novel networking frequency spectrum sharing method and system based on a convergence station and an AI base station, and aims to solve the interference problem existing in frequency spectrum sharing. The method comprises the following steps: determining a networking architecture based on a convergence station and an AI base station; according to the networking architecture, determining a networking system model based on a convergence station and an AI base station; the networking system model comprises a periodically updated wireless electromagnetic map; determining a resource configuration scheme of the current time slot according to a wireless electromagnetic map included in the networking system model; the resource configuration scheme of the current time slot is determined by taking maximization of the minimum terminal capacity in the networking architecture as a target; the terminal capacity represents the data transmission capability of the terminal; and according to 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 network of a convergence station and an AI base station. Background Art

[0002] The aggregation station is a relatively high new type of elevated wireless backhaul site, and the AI ​​base station is a new type of base station with edge computing capabilities that can be flexibly deployed. Comprehensively comparing the coverage, anti-destruction, latency, spectrum efficiency, and power efficiency, the aggregation station is more cost-effective and easier to maintain than the traditional satellite network. The AI ​​base station network is more flexible than the ground network and can be flexibly deployed on demand in multiple regions and platforms.

[0003] However, there are still many technical problems to be solved 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] In order to overcome the problems existing in the related technologies, the present disclosure provides a spectrum sharing method and system based on a new type of networking of a convergence station and an AI base station. The technical solution of the present disclosure is as follows: According to a first aspect of an embodiment of the present disclosure, a spectrum sharing method based on a new type of networking of a convergence station and an AI base station is provided, including: Determine the networking architecture based on aggregation stations and AI base stations; According to the networking architecture, a networking system model based on a convergence station and an AI base station is determined; the networking system model includes a periodically updated radio electromagnetic map; Determine a resource allocation scheme for a current time slot according to 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; According to the resource configuration scheme, an anti-interference scheme for reducing spectrum sharing interference in the current time slot is determined.

[0005] 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: Moving the position of the AI ​​base station in the networking system model; According to the real-time movement of the AI ​​base station in the networking system model, the radio electromagnetic map is updated in real time; Calculate 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 strategy, determining a terminal with the smallest terminal capacity among the terminals as a 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 of the AI ​​base stations in the current time slot when the terminal capacity of the target terminal reaches a maximum value; According to the target position of the AI ​​base station in the current time slot, a resource configuration scheme for the current time slot is determined.

[0006] Optionally, the capacity of each terminal is calculated by the network controller in combination with the radio electromagnetic map updated in real time, including: Determine, according to the radio electromagnetic map, a first signal-to-noise ratio corresponding to each of the aggregation stations, and determine a second signal-to-noise ratio corresponding to each of the AI ​​base stations; Determine a first total throughput of the communication link from the aggregation station to the AI ​​base station according to each of the first signal-to-noise ratios; 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; Determine, according to 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: 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; Determine, 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.

[0007] Optionally, the resource configuration scheme includes a physical position of the AI ​​base station in the current time slot; in the case of spectrum sharing in combination with a satellite network, determining an anti-interference scheme for reducing spectrum sharing interference in the current time slot according to the resource configuration scheme, including: Determine the interference type of spectrum sharing interference; the interference type includes intra-system interference and inter-system interference; the system interference characterization: interference between the aggregation station and the AI ​​base station in the networking architecture; the inter-system interference characterization: interference between the networking architecture and the satellite network; Determine the physical location of the AI ​​base station in the current time slot in the resource configuration scheme, and determine the antenna pattern of the aggregation station in the current time slot; According to the geographical location relationship between the aggregation station and the AI ​​base station, the aggregation station communicating with the AI ​​base station is adjusted; by adjusting the aggregation station communicating with the AI ​​base station, interference within the system is reduced; According to the geographical location information and the resource allocation scheme, the timing when the interference between systems exceeds the interference threshold is predicted; and the interference between systems is reduced through interference threshold constraints.

[0008] Optionally, 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: Determine the 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 without obstacles between the AI ​​base station and 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.

[0009] 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: According to the phased array multi-beam antenna adopted by the aggregation station in the networking architecture, a first coverage radius of the aggregation station is determined; the networking system model determines a first core factor of the aggregation station contained in the first coverage radius around the first coverage radius; Determine a second coverage radius of the AI ​​base station according to the moving antenna used by the AI ​​base station in the networking architecture; determine, by the networking system model, a second core factor of the AI ​​base station contained in the second coverage radius around the second coverage radius; Among them, the terminals covered by each of the AI ​​base stations 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.

[0010] Optionally, it also includes: The networking architecture based on the aggregation station and the AI ​​base station uses directional antenna communication to achieve wide-area coverage without interfering with the satellite network. By adopting directional antenna communication, wide-area coverage is achieved, improving spectrum utilization and power efficiency.

[0011] According to a second aspect of an embodiment of the present disclosure, a spectrum sharing system based on a new type of networking of an aggregation station and an AI base station is provided, which is applied to the spectrum sharing method based on a new type of networking of an aggregation station and an AI base station described in the first aspect, including: A plurality of aggregation stations, wherein the plurality of aggregation stations are used to provide services for 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 networking architecture. The network controller is used to calculate the corresponding resource configuration plan according to the radio electromagnetic map.

[0012] Optionally, it also includes: The networking architecture corresponding to the new networking 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.

[0013] According to a third aspect of an embodiment of the present disclosure, a spectrum sharing device based on a new type of networking of a convergence station and an AI base station is provided, including: A 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, used 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 configuration scheme determination module is used to determine the resource configuration scheme of the current time slot according to the radio electromagnetic map included in the networking system model; the resource configuration scheme of 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; The anti-interference scheme determination module is used to determine the anti-interference scheme for reducing spectrum sharing interference in the current time slot according to the resource configuration scheme.

[0014] 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, wherein when the computer program is executed by the processor, the steps of the spectrum sharing method based on the new networking of a convergence station and an AI base station as described in the first aspect are implemented.

[0015] 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.

[0016] 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 an aggregation station and an AI base station described in the first aspect are implemented.

[0017] The present disclosure can make full use of 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 by rationally designing the networking architecture. Through periodically updated radio electromagnetic maps, the 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, the 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 requirements. 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

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the description of the embodiments of the present disclosure will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 1 is a schematic diagram of a networking architecture based on a convergence station and an AI base station shown in an embodiment of the present disclosure; Figure 2 It 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 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 4 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 5It is a block diagram of a novel networking and spectrum sharing device based on a convergence station and an AI base station shown in an embodiment of the present disclosure; Figure 6 It is a schematic diagram of an electronic device shown in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0021] 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 when appropriate, 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 associated with each other are in an "or" relationship.

[0022] The aggregation station is a relatively high new type of elevated wireless backhaul site, and the AI ​​base station is a new type of base station with edge computing capabilities that can be flexibly deployed. The AI ​​base station network can break through the constraints of power / optical fiber / tower infrastructure and be deployed on demand in multiple regions and platforms. For example, when natural disasters such as earthquakes and fires occur, mobile AI base stations can be flexibly deployed on demand in areas that are not covered by ground mobile base stations to achieve emergency communications.

[0023] Taking into account comprehensive comparison of coverage, anti-destruction, latency, spectrum efficiency, power efficiency and other performance, aggregation stations are more cost-effective and easier to maintain than traditional satellite networks. AI base station networking is more flexible than ground networks and can be deployed on demand in multiple regions and platforms.

[0024] 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.

[0025] 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, wherein the multiple aggregation stations are used to provide services to AI base stations; 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; a knowledge base is used to generate a corresponding radio electromagnetic map according to the information of each device in the networking architecture, and a network controller is used to calculate the corresponding resource configuration plan according to the radio electromagnetic map.

[0026] This type of networking includes multiple aggregation stations and multiple AI base stations.

[0027] The aggregation station is an elevated wireless backhaul site that can provide extensive wireless coverage, using 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 effective data transmission.

[0028] AI base stations can be flexibly deployed in multiple regions and platforms according to demand, especially in areas that traditional base stations cannot cover, to provide necessary communication services. AI base stations are equipped with edge computing units that can process data and perform computing tasks locally, reduce data transmission delays, improve response speed, and adapt to the needs of real-time applications. AI base stations are responsible for providing access services to users, and by working in collaboration with aggregation stations, ensure that users can obtain stable signals and good communication quality.

[0029] The AI ​​base station exchanges information with the aggregation station through the backhaul link.

[0030] This type of networking also includes a knowledge base and a network controller. The knowledge base can be responsible for collecting various information about user terminals, including location information, service priority, service type, interference factors, etc. The knowledge base can quickly process and update information, and use the real-time uploaded information to generate and update the radio electromagnetic map to ensure real-time decision-making. The radio electromagnetic map can reflect the signal coverage and interference level in different areas, providing a basis for the optimal configuration of spectrum resources. 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 the use of spectrum based on the real-time data and models provided by the knowledge base to improve the overall performance and spectrum utilization of the network. The network controller can dynamically schedule resources between the aggregation station and the AI ​​base station according to user needs and network status to ensure that users can get good services at different times and locations.

[0031] Figure 1 FIG. 1 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 1As 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, wherein 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, and the network controller is used to calculate the corresponding resource allocation plan based on the radio electromagnetic map.

[0032] By adopting the embodiments of the present disclosure, the new networking based on the aggregation station and the AI ​​base station can effectively alleviate the interference during spectrum sharing and improve the overall network performance and user experience through the advantages of flexible deployment, efficient spectrum utilization, enhanced anti-destruction and intelligent interference management. The new networking based on the aggregation station and the AI ​​base station can achieve high spectrum reuse gain and improve spectrum utilization, thereby maximizing the overall performance of the network.

[0033] 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, can 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.

[0034] Figure 2 FIG. 1 is a schematic diagram of the steps of a spectrum sharing method based on a new type of network of a convergence station and an AI base station according to an embodiment of the present disclosure. Figure 1 As shown, the method may specifically include the following steps: Step S11: Determine a networking architecture based on a convergence station and an AI base station.

[0035] The aggregation station is an elevated base station that can provide wide-area coverage and stable wireless signal backhaul. The coverage radius of the aggregation station can effectively connect multiple AI base stations. The AI ​​base station is a base station that can be flexibly deployed and has edge computing capabilities. The deployment flexibility of the AI ​​base station enables it to respond quickly in a variety of environments, and it can efficiently allocate resources and manage beams 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.

[0036] Determine the network coverage, number of terminals, data transmission rate and other requirements to determine the basic parameters of the networking architecture. According to the requirements, select appropriate wireless communication technologies, such as 5G, Wi-Fi, etc., 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.

[0037] 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.

[0038] Step S12: According to the networking architecture, a networking system model based on a convergence station and an AI base station is determined; the networking system model includes a periodically updated radio electromagnetic map.

[0039] Utilize spectrum sensing, geographic location information database and other technologies to monitor and sense the radio electromagnetic environment in real time and establish a radio electromagnetic map that includes spectrum resource utilization. Update the radio electromagnetic map periodically based on the dynamic changes in spectrum resources to ensure the accuracy and real-time nature of the map. Based on the radio electromagnetic map, build a networking system model that includes aggregation stations, AI base stations and terminals. The networking system model can simulate the data transmission and spectrum sharing process in the network.

[0040] Step S13: Determine the resource allocation scheme for the current time slot according to 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.

[0041] According to the networking system model, analyze the data transmission capability of each terminal, that is, the terminal capacity. With the goal of maximizing the minimum terminal capacity in the networking architecture, ensure that each terminal in the network can obtain sufficient resource support. According to the optimization goal and the radio electromagnetic map, formulate a resource allocation plan for the current time slot.

[0042] Step S14: According to the resource configuration scheme, determine an anti-interference scheme for reducing spectrum sharing interference in the current time slot.

[0043] According to the resource allocation plan and radio electromagnetic map, analyze the possible interference sources and interference levels in the spectrum sharing process. According to the interference sources and interference levels, formulate appropriate anti-interference strategies. Through real-time monitoring of network performance and spectrum utilization, the effectiveness of the anti-interference plan can be evaluated, and necessary adjustments and optimizations can be made based on the evaluation results.

[0044] 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 configuration and anti-interference strategies. The radio electromagnetic map is used to determine the resource configuration 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 the terminals in the entire network can obtain relatively balanced service quality. According to the resource configuration scheme, the anti-interference scheme for reducing spectrum sharing interference in the current time slot is determined, which can be flexibly adjusted according to the specific network environment 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.

[0045] 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; determines the second coverage radius of the AI ​​base station according to the moving 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.

[0046] 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, beam pointing, etc., and environmental factors such as atmospheric attenuation and terrain influence.

[0047] According to 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 redundancy to cope with factors such as environmental changes and equipment aging.

[0048] Collect data on the first core factors such as the latitude and longitude position, altitude, beam parameters, and transmission power of the aggregation station; the beam parameters include beam width, beam pointing, and number of beams. Sort and analyze each first core parameter to ensure the accuracy and completeness of the first core parameters corresponding to each aggregation station. Determine the collected first core parameters of each aggregation station as the first core parameters corresponding to each aggregation station in the networking system model.

[0049] The AI ​​base station uses a mobile antenna, and measures the coverage of the AI ​​base station based on the characteristics of the mobile antenna, such as antenna gain, beam pointing adjustment capability, etc. Since the AI ​​base station is deployed in a mobile manner, its coverage may change with the change of location, and the measurement results of the AI ​​base station coverage can be dynamically adjusted.

[0050] According to 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.

[0051] Collect data on the second core factors such as the movement trajectory, beam parameters, and transmission power of each AI base station; beam parameters include beam width, beam pointing, etc. The second core factors of each AI base station need to be updated and analyzed in real time to reflect the dynamic changes of the AI ​​base station. The collected second core factors of each AI base station are determined as the second core parameters corresponding to each AI base station in the networking system model.

[0052] The terminals covered by each AI base station upload their corresponding terminal information to the knowledge base through the AI ​​base station. The terminal information includes location information, service priority, service type, interference factors, etc. When each terminal collects and uploads terminal information, it does so by using its own configured environmental information collection unit.

[0053] The knowledge base conducts comprehensive analysis and calculation based on the received terminal information, the first core factor of the aggregation station, and the second core factor of the AI ​​base station. When calculating, the channel model, such as the three-state model based on the Markov chain, and the signal fading characteristics, such as free space path loss and shadow fading, are also considered to generate a radio electromagnetic map corresponding to the networking system model. The radio electromagnetic map should be able to reflect the electromagnetic environment, signal strength, interference and other information in the network.

[0054] By adopting the embodiments of the present disclosure, the networking system model corresponding to the networking architecture is determined by real-time collection and analysis of environmental and business information, 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 transmit 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.

[0055] 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.

[0056] When designing and implementing a networking architecture based on aggregation stations and AI base stations, it is necessary to ensure that there is no interference with existing satellite network communications, while using directional antenna communication technology to achieve wider network coverage.

[0057] 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.

[0058] Directional antennas can focus signals in a specific direction instead of radiating in all directions, allowing signals to be transmitted to the target receiver more efficiently while reducing interference in other directions, so that spectrum resources in the same frequency band can be used more effectively, thereby improving the efficiency of spectrum use. The networking architecture based on aggregation stations and AI base stations built using directional antennas can achieve a wider coverage range while ensuring signal strength and quality, thereby effectively serving more users while reducing interference.

[0059] 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 and reduce co-channel interference, so that spectrum resources in the same frequency band can be shared by multiple users or devices, improving the overall utilization of the spectrum. Directional antenna technology can provide strong signal coverage in a wider area, effectively overcome signal attenuation caused by obstacles, and can achieve longer transmission distances and better signal quality at lower transmission power. 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.

[0060] The networking architecture supports 4G / 5G collaborative deployment, which allows users to obtain services under different network conditions and take advantage of the respective advantages of 4G and 5G. This is because existing mobile phones may only support 4G networks or support both 4G and 5G networks. Through the collaborative deployment of 4G / 5G, various devices can get good services.

[0061] Seamless switching means that users can automatically switch between existing base stations and AI base stations during movement without interrupting current communications or data transmission. And this seamless switching is automatic, and users do not need to operate manually, so that they can maintain 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 provide higher speeds and lower latency. Supporting seamless switching allows users to enjoy the best service under different network conditions.

[0062] By adopting the embodiments of the present disclosure, the coordinated deployment of 4G and 5G base stations can cover a wider area, and existing mobile phones can also obtain stable network connections. According to the user's mobility and network conditions, the best base station can be dynamically selected through seamless switching to ensure that users can obtain good network services in different environments.

[0063] 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 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 through the network controller in combination with the radio electromagnetic map updated in real time; 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; 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.

[0064] The networking architecture also includes a network controller, which can calculate a resource configuration plan corresponding to the radio electromagnetic map based on the radio electromagnetic map of the current time slot.

[0065] In the networking system model, the initial position of the AI ​​base station in the time slot is determined. These positions can be preset, based on historical data, or dynamically adjusted according to current needs.

[0066] Get the radio electromagnetic map of the current time slot, which reflects the electromagnetic conditions in the network environment.

[0067] 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.

[0068] The network controller combines the real-time updated radio electromagnetic map and uses the corresponding algorithm or model to calculate the terminal capacity of each terminal. The terminal capacity calculated at this time reflects the ability of the terminal to transmit data in the current electromagnetic environment.

[0069] Applying the terminal fairness strategy can ensure that each terminal in the network can obtain relatively fair services. The core is to focus on the terminal with the smallest terminal capacity in the Zuwang architecture and maximize the terminal capacity corresponding to the terminal so that the terminal capacity of the remaining terminals can meet the demand.

[0070] Through the terminal fairness strategy, the terminal with the smallest terminal capacity among all terminals is determined as the target terminal. The target terminal will be updated as the AI ​​base station moves. Specifically, if the capacity of a new terminal is smaller than the capacity of the current target terminal during the movement of the AI ​​base station, the target terminal will be updated to the new minimum capacity terminal.

[0071] When the terminal capacity of the target terminal is maximized, the position of the AI ​​base station is adjusted through an algorithm or optimization model. Multiple iterations can be performed until the position of the AI ​​base station that maximizes the target terminal capacity is found, and the position of the AI ​​base station that maximizes the target terminal capacity is determined as the target position of the AI ​​base station in the current time slot. The target position of the AI ​​base station means that the AI ​​base station will be at the target position to provide services to the terminal in this time slot.

[0072] According to the target position of the AI ​​base station in the current time slot, combined with other information in the network controller, such as business requirements, spectrum resources, etc., the resource configuration plan for the current time slot is determined. The resource configuration plan may include spectrum allocation, power control, access strategy and other aspects.

[0073] Implement the determined resource allocation plan into the networking architecture to optimize network performance and user experience. After the resource allocation plan is implemented into the networking architecture, network performance can be monitored in real time, including indicators such as terminal capacity, spectrum utilization efficiency, and network latency. According to the monitoring results, the resource allocation plan can be adjusted and optimized as necessary.

[0074] 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 strategy, 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 the optimal utilization of network resources.

[0075] Among them, 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 the first signal-to-noise ratio corresponding to each of the aggregation stations according to the radio electromagnetic map, and determining the second signal-to-noise ratio corresponding to each of the AI ​​base stations; determining the first total throughput of the communication link from the aggregation station to the AI ​​base station according to each of the first signal-to-noise ratios; 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 the second total throughput of the communication link from the AI ​​base station to the terminal according to each of the second signal-to-noise ratios; 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 the first capacity according to the first total throughput and the number of terminals; determining the second capacity of the AI ​​base station to each of the terminals according to 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.

[0076] The location information of each aggregation station and AI base station as well as corresponding parameters such as signal strength and interference level are extracted from the radio electromagnetic map updated in real time.

[0077] 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 may be evaluated, and network optimization and resource allocation may be performed accordingly.

[0078] According to 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, and each first signal-to-noise ratio reflects the signal quality of the communication link from the aggregation station to the AI ​​base station.

[0079] 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 calculated using express:

[0080] 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. The 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 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, is the available bandwidth of AI base station user u. 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.

[0081] According to 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, and each second signal-to-noise ratio reflects the signal quality of the communication link from the AI ​​base station to the terminal.

[0082] 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 calculated using express:

[0083] 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 transmission 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 It 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.

[0084] Based on the first signal-to-noise ratio 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 capability from each aggregation station to each AI base station.

[0085] The following is a specific calculation formula for the first total throughput. The first total throughput can be calculated using To express:

[0086] 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.

[0087] 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 from each AI base station to each terminal.

[0088] The following is a specific calculation formula for the second total throughput. The second total throughput can be calculated using To express:

[0089] in, It is the channel allocation factor from AI base station to terminal.

[0090] It is the total capacity of the networking architecture. and It conforms to the 0-1 binomial distribution.

[0091] 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 value 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.

[0092] Based on this, the objective function of the AI ​​base station location optimization problem can be mathematically modeled as shown in the formula:

[0093] Through the mathematical modeling formula corresponding to the objective function, the target terminal with the smallest terminal capacity among the 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.

[0094] 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. The calculation is based on the real-time updated radio electromagnetic map, so it can respond quickly to changes in the network environment. When new interference sources appear in the network, the terminal moves, or the 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.

[0095] 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 is no obstacle blocking the communication link from the aggregation station to the AI ​​base station.

[0096] Figure 4 FIG. 1 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 1, through terminal fair measurement, the position of each AI base station in the networking architecture is determined in this time slot. The position of the AI ​​base station and the aggregation station is Figure 4 It is specifically represented by the horizontal and vertical axes.

[0097] After determining the position of the AI ​​base station in the networking architecture at a certain time slot, the anti-interference scheme for reducing spectrum sharing interference at that time slot is then determined.

[0098] Among them, in an optional embodiment, the resource configuration scheme includes the physical position 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 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 position 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 that communicates 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 that communicates 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.

[0099] In the case of applying the networking architecture based on aggregation stations and AI base stations proposed in the present 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 a satellite network.

[0100] The resource allocation scheme can determine the physical location of the AI ​​base station in the current time slot and the antenna pattern. The antenna pattern can reflect the communication direction between the aggregation station and the AI ​​base station, and analyze the signal transmission path and possible interference between the aggregation station and the AI ​​base station based on the antenna pattern; the antenna pattern data of the aggregation station includes parameters such as antenna gain and beam width.

[0101] Based on the geographical location relationship between the AI ​​base station and the aggregation station, analyze the causes and impact range of interference within the system. According to the interference analysis results, formulate an adjustment strategy for the aggregation station, including adjusting the antenna direction, transmission power, etc. Implement the adjustment strategy, and monitor the adjusted signal transmission quality and interference in real time. Specifically, the aggregation station that communicates with the AI ​​base station can be appropriately adjusted. For example, if the interference is large when the AI ​​base station communicates with aggregation station 1, then adjust the AI ​​base station to communicate with aggregation station 2, thereby improving the communication effect from the aggregation station to the AI ​​base station. This reduces interference within the system.

[0102] Use geographic location information and resource allocation plans, combined with historical data or prediction models, to predict when the interference between systems exceeds the interference threshold. Analyze the prediction results to determine the source of interference and the scope of influence. The operating frequency of the satellite network can be adjusted, the network architecture can be optimized, etc. After implementing the anti-interference plan for inter-system interference, the interference situation can be continuously monitored to ensure that the interference between systems remains within an acceptable range.

[0103] 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 maintained 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.

[0104] Among them, in an optional embodiment, adjusting the aggregation station that communicates with the AI ​​base station according to 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 that 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.

[0105] 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.

[0106] 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.

[0107] For each AI base station, select the aggregation station with no obstacles between it and the aggregation station selected at this time, and determine the aggregation station selected at this time as the first aggregation station. Determine whether each AI base station is communicating with the first aggregation station through the antenna pattern. If not, adjust the AI ​​base station to communicate with the first aggregation station selected, so that there are no obstacles between the AI ​​base station and the aggregation station in communication. This is because the loss of obstacles will cause the signal energy to be greatly 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 other technical means, such as relay stations, to overcome the obstacles.

[0108] In the case where 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, network load, etc. can be considered to make the final selection.

[0109] 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 with the second aggregation station smoothly. During the communication between the AI ​​base station and the second aggregation station, monitor the quality of the communication link and make adjustments and optimizations as needed.

[0110] By adopting the embodiments of the present disclosure, by screening out the aggregation station with unobstructed 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, which not only reduces the distance of signal transmission and reduces 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.

[0111] Figure 5 is a block diagram of a spectrum sharing device 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 5 As shown, the device comprises: A network architecture determination module 510, configured to determine a network architecture based on a convergence station and an AI base station; A networking system model determination module 520 is used 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; The resource configuration scheme determination module 530 is used to determine the resource configuration scheme of the current time slot according to the radio electromagnetic map included in the networking system model; the resource configuration scheme of 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; The anti-interference scheme determining module 540 is used to determine an anti-interference scheme for reducing spectrum sharing interference in the current time slot according to the resource configuration scheme.

[0112] Optionally, the networking architecture includes a network controller, and the resource configuration scheme determination module is specifically configured to execute: Moving the position of the AI ​​base station in the networking system model; According to the real-time movement of the AI ​​base station in the networking system model, the radio electromagnetic map is updated in real time; Calculate 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 strategy, determining a terminal with the smallest terminal capacity among the terminals as a 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 of the AI ​​base stations in the current time slot when the terminal capacity of the target terminal reaches a maximum value; According to the target position of the AI ​​base station in the current time slot, a resource configuration scheme for the current time slot is determined.

[0113] Optionally, the resource configuration scheme determination module is specifically used to execute: Determine, according to the radio electromagnetic map, a first signal-to-noise ratio corresponding to each of the aggregation stations, and determine a second signal-to-noise ratio corresponding to each of the AI ​​base stations; Determine a first total throughput of the communication link from the aggregation station to the AI ​​base station according to each of the first signal-to-noise ratios; 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; Determine, according to 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: 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; Determine, 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.

[0114] Optionally, the resource configuration scheme includes a physical location of the AI ​​base station in the current time slot; in the case of spectrum sharing in combination with a satellite network, 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 system interference characterization: interference between the aggregation station and the AI ​​base station in the networking architecture; the inter-system interference characterization: interference between the networking architecture and the satellite network; Determine the physical location of the AI ​​base station in the current time slot in the resource configuration scheme, and determine the antenna pattern of the aggregation station in the current time slot; According to the geographical location relationship between the aggregation station and the AI ​​base station, the aggregation station communicating with the AI ​​base station is adjusted; by adjusting the aggregation station communicating with the AI ​​base station, interference within the system is reduced; According to the geographical location information and the resource allocation scheme, the timing when the interference between systems exceeds the interference threshold is predicted; and the interference between systems is reduced through interference threshold constraints.

[0115] Optionally, the anti-interference scheme determination module is specifically used to execute: Determine the 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 without obstacles between the AI ​​base station and 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.

[0116] Optionally, the networking architecture includes a knowledge base, and the networking system model determination module is specifically used to execute: According to the phased array multi-beam antenna adopted by the aggregation station in the networking architecture, a first coverage radius of the aggregation station is determined; the networking system model determines a first core factor of the aggregation station contained in the first coverage radius around the first coverage radius; Determine a second coverage radius of the AI ​​base station according to the moving antenna used by the AI ​​base station in the networking architecture; determine, by the networking system model, a second core factor of the AI ​​base station contained in the second coverage radius around the second coverage radius; Among them, the terminals covered by each of the AI ​​base stations 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.

[0117] Optionally, the device further comprises: 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, the spectrum utilization and power efficiency are improved.

[0118] Optionally, the network architecture determination module is specifically used to execute: The networking architecture 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.

[0119] The present disclosure also provides an electronic device, referring to Figure 6 , Figure 6 is a schematic diagram of an electronic device shown in an embodiment of the present disclosure. Figure 6 As 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. 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.

[0120] The embodiments of the present disclosure also provide 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 embodiments of the present disclosure are implemented.

[0121] 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, the steps in the spectrum sharing method based on the new networking of an aggregation station and an AI base station as disclosed in the embodiments of the present disclosure are implemented.

[0122] 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.

[0123] It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, devices or computer program products. Therefore, the embodiments of the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] 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 the 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 generate 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.

[0125] These computer program instructions may also be stored in a computer readable memory capable of directing 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 A function specified in one or more boxes.

[0126] 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.

[0127] 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.

[0128] The above is a detailed introduction to a 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. This article uses specific examples 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 idea; at the same time, for general technical personnel in this field, 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; According to the networking architecture, a networking system model based on a convergence station and an AI base station is determined; the networking system model includes a periodically updated radio electromagnetic map; Determine a resource allocation scheme for a current time slot according to 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; According to the resource configuration scheme, an anti-interference scheme for reducing spectrum sharing interference in the current time slot is determined.

2. The method according to claim 1, characterized in that 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: Moving the position of the AI ​​base station in the networking system model; According to the real-time movement of the AI ​​base station in the networking system model, the radio electromagnetic map is updated in real time; Calculate 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 strategy, determining a terminal with the smallest terminal capacity among the terminals as a 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 of the AI ​​base stations in the current time slot when the terminal capacity of the target terminal reaches a maximum value; According to the target position of the AI ​​base station in the current time slot, a resource configuration scheme for the current time slot is determined.

3. The method according to claim 2, characterized in that The capacity of each terminal is calculated by the network controller in combination with the radio electromagnetic map updated in real time, including: Determine, according to the radio electromagnetic map, a first signal-to-noise ratio corresponding to each of the aggregation stations, and determine a second signal-to-noise ratio corresponding to each of the AI ​​base stations; Determine a first total throughput of the communication link from the aggregation station to the AI ​​base station according to each of the first signal-to-noise ratios; 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; Determine, according to 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: 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; Determine, 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.

4. The method according to claim 1, characterized in that The resource configuration scheme includes a physical location of the AI ​​base station in the current time slot; in the case of spectrum sharing in combination with a satellite network, an anti-interference scheme for reducing spectrum sharing interference in the current time slot is determined according to the resource configuration scheme, including: Determine the interference type of spectrum sharing interference; the interference type includes intra-system interference and inter-system interference; the system interference characterization: interference between the aggregation station and the AI ​​base station in the networking architecture; the inter-system interference characterization: interference between the networking architecture and the satellite network; Determine the physical location of the AI ​​base station in the current time slot in the resource configuration scheme, and determine the antenna pattern of the aggregation station in the current time slot; According to the geographical location relationship between the aggregation station and the AI ​​base station, the aggregation station communicating with the AI ​​base station is adjusted; by adjusting the aggregation station communicating with the AI ​​base station, interference within the system is reduced; According to the geographical location information and the resource allocation scheme, the timing when the interference between systems exceeds the interference threshold is predicted; and the interference between systems is reduced through interference threshold constraints.

5. The method according to claim 4, 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: Determine the 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 without obstacles between the AI ​​base station and 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.

6. The method according to claim 1, characterized in that 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: According to the phased array multi-beam antenna adopted by the aggregation station in the networking architecture, a first coverage radius of the aggregation station is determined; the networking system model determines a first core factor of the aggregation station contained in the first coverage radius around the first coverage radius; Determine a second coverage radius of the AI ​​base station according to the moving antenna used by the AI ​​base station in the networking architecture; determine, by the networking system model, a second core factor of the AI ​​base station contained in the second coverage radius around the second coverage radius; Among them, the terminals covered by each of the AI ​​base stations 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.

7. The method according to any one of claims 1 to 6, characterized in that: Also includes: The networking architecture based on the aggregation station and the AI ​​base station uses directional antenna communication to achieve wide-area coverage without interfering with the satellite network. By adopting directional antenna communication, wide-area coverage is achieved, improving spectrum utilization and power efficiency.

8. A spectrum sharing system based on a new type of networking of a convergence station and an AI base station, characterized in that: The method applied to any one of claims 1 to 7 comprises: A plurality of aggregation stations, wherein the plurality of aggregation stations are used to provide services for 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 networking architecture. The network controller is used to calculate the corresponding resource configuration plan according to the radio electromagnetic map.

9. The system according to claim 8, 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.

10. A spectrum sharing device based on a new type of networking of a convergence station and an AI base station, characterized in that: include: A 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, used 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 configuration scheme determination module is used to determine the resource configuration scheme of the current time slot according to the radio electromagnetic map included in the networking system model; the resource configuration scheme of 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; The anti-interference scheme determination module is used to determine the anti-interference scheme for reducing spectrum sharing interference in the current time slot according to the resource configuration scheme.

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