Dynamic control method and device for base station of heterogeneous wireless networking, electronic equipment and medium

Abstract

The application discloses a dynamic control method and device of a base station of a heterogeneous wireless network, electronic equipment and a medium. Through application of the technical solution of the application, each data base station can feed back the traffic demand of the next moment predicted by the data base station based on the real-time mobile position of a user and a traffic prediction model to a control base station in the heterogeneous wireless network. The control base station can adjust the running state of one or more data base stations based on the overall traffic demand of the data base stations in the network. Thus, on the one hand, the data base station can combine the mobile position of the user when predicting the traffic to improve the prediction accuracy. On the other hand, the globality and stability of the regional network traffic distribution prediction are ensured, and the opening and closing of the data base station are dynamically controlled according to the real-time traffic demand of the network, so that the network energy consumption is reduced.

Description

Technical Field

[0001] This application relates to wireless communication processing technology, and in particular to a method, apparatus, electronic device and medium for dynamic control of base stations in heterogeneous wireless networking. Background Technology

[0002] Heterogeneous, multi-layered, and full-band-accessible high- and low-frequency cooperative wireless networking is an inevitable trend in future 6G network architecture. Low-frequency bands primarily address coverage issues, while high-frequency bands are mainly used to improve system capacity in high-traffic areas. 6G networks will introduce a decoupling mechanism between control signaling and service data. Control base stations will operate in low-frequency bands to provide users with wide-coverage control signaling services, while data base stations will operate in high-frequency bands to provide users with high-capacity service data.

[0003] Furthermore, to reduce network power consumption and cost issues caused by the dense deployment of data base stations in high- and low-frequency wireless cooperative networking, related technologies periodically shut down some base stations to avoid unnecessary energy consumption, thereby reducing network operating costs. However, existing base station control methods are very fixed and cannot flexibly and intelligently control the activation and deactivation of base stations based on network conditions. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and medium for dynamic control of base stations in a heterogeneous wireless network. It addresses the problem in related technologies where no system can intelligently control the activation and deactivation of individual base stations in a heterogeneous wireless network.

[0005] According to one aspect of the embodiments of this application, a method for dynamic control of base stations in a heterogeneous wireless network is provided. The heterogeneous wireless network includes a control base station providing data services in a first frequency band and a data base station providing data services in a second frequency band, wherein the second frequency band is higher than the first frequency band.

[0006] Each data base station, based on a traffic prediction model and a mobility prediction model, predicts its own traffic demand in a future time period and then sends the traffic demand to the control base station.

[0007] The control base station inputs the traffic demand of each data base station into a pre-built dynamic switching decision model to obtain a switching decision. The switching decision includes indication information for indicating whether at least one data base station provides data services.

[0008] The control base station sends the switching decision to the data base station, so that the data base station can determine whether to provide data services based on the indication information in the switching decision.

[0009] Optionally, in another embodiment based on the method described above in this application, the prediction of one's own traffic demand in a future time period includes:

[0010] The data base station sends a model delivery request to the control base station, the model delivery request being used to request the control base station to send an initial traffic prediction model and an initial mobility prediction model;

[0011] The data base station predicts the traffic demand based on the received initial traffic prediction model and initial mobility prediction model.

[0012] Optionally, in another embodiment based on the method described above in this application, after the data base station sends a model distribution request to the control base station, the method further includes:

[0013] The data base station trains the initial traffic prediction model based on the locally stored historical network traffic dataset to obtain a trained and updated traffic prediction model; and the data base station trains the initial mobility prediction model based on the locally stored historical mobility trajectory dataset to obtain a trained and updated mobility prediction model.

[0014] After the data base station sends the trained and updated traffic prediction model and the trained and updated mobility prediction model to the control base station, the control base station stores the trained and updated traffic prediction model and the trained and updated mobility prediction model.

[0015] The data base station receives the aggregated traffic prediction model and the aggregated mobility prediction model sent by the control base station.

[0016] Optionally, in another embodiment based on the method described above in this application, after the data base station sends the trained and updated traffic prediction model and the trained and updated mobility prediction model to the control base station, the method further includes:

[0017] The control base station aggregates the trained and updated traffic prediction models sent by each data base station to obtain the aggregated traffic prediction model; and aggregates the trained and updated mobility prediction models sent by each data base station to obtain the aggregated mobility prediction model.

[0018] The control base station distributes the aggregated traffic prediction model and the aggregated mobility prediction model to each data base station;

[0019] The data base station predicts the traffic demand based on the aggregated traffic prediction model and the aggregated mobility prediction model.

[0020] Optionally, in another embodiment based on the method described above, each data base station predicts its own traffic demand in a future time period based on a traffic prediction model and a mobility prediction model, including:

[0021] The data base station predicts its initial traffic demand in the future time period based on the traffic prediction model; and predicts the user location of the users served by the data base station in the future time period based on the mobility prediction model.

[0022] Based on the user's location, the initial traffic demand is adjusted to obtain the user's traffic demand in the future time period.

[0023] Optionally, in another embodiment based on the method described above in this application, the control base station sends the switching decision to the data base station, including:

[0024] The control base station sends the switching decision to each data base station in the heterogeneous wireless network; or...

[0025] The control base station sends the switching decision to the first data base station in the heterogeneous wireless network. The first data base station is the data base station that needs to stop or start providing data services, as reflected by the switching decision.

[0026] Optionally, in another embodiment based on the method described above in this application, after the control base station sends the switching decision to the data base station, the method further includes:

[0027] The control base station sends a handover broadcast notification to the wireless terminal providing data services. The handover broadcast notification is used to inform the wireless terminal to perform data transmission services with other data base stations.

[0028] After determining that the wireless terminal has connected to the other data base station, the data base station controls itself to shut down.

[0029] According to another aspect of the embodiments of this application, a base station dynamic control device for a heterogeneous wireless network is provided. The heterogeneous wireless network includes a control base station providing data services in a first frequency band and a data base station providing data services in a second frequency band, wherein the second frequency band is higher than the first frequency band.

[0030] The prediction module is configured so that each data base station, based on a traffic prediction model and a mobility prediction model, predicts its own traffic demand in a future time period and then sends the traffic demand to the control base station.

[0031] The generation module is configured such that the control base station inputs the traffic requirements of each data base station into a pre-built dynamic switching decision model to obtain a switching decision, the switching decision including indication information for indicating whether at least one data base station provides data service;

[0032] The control module is configured such that the control base station sends the switching decision to the data base station, so that the data base station determines whether to provide data services based on the indication information in the switching decision.

[0033] According to another aspect of the embodiments of this application, an electronic device is provided, comprising:

[0034] Memory, used to store executable instructions; and

[0035] A display is used in conjunction with the memory to execute the executable instructions to perform the operation of the base station dynamic control method for any of the heterogeneous wireless networks described above.

[0036] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided for storing computer-readable instructions, which, when executed, perform the operation of any of the above-described heterogeneous wireless network base station dynamic control methods.

[0037] In this application, each data base station can predict its own traffic demand for a future time period based on a traffic prediction model and a mobility prediction model, and then send the traffic demand to a control base station. The control base station inputs the traffic demand of each data base station into a pre-built dynamic switching decision model to obtain a switching decision. The switching decision includes indication information for indicating whether at least one data base station should provide data service. The control base station then distributes the switching decision to the data base stations, enabling them to determine whether to provide data service based on the indication information in the switching decision. By applying the technical solution of this application, each data base station can predict its own traffic demand for the next time period based on the user's real-time mobility location and the traffic prediction model, and then feed this prediction back to the control base station in the heterogeneous wireless network. This allows the control base station to adjust the operating status of one or more data base stations accordingly based on the overall traffic demand of the data base stations in the network. This achieves the goal of improving the accuracy of traffic prediction by incorporating user mobility location during traffic prediction. Furthermore, it ensures the globality and stability of the regional network traffic distribution prediction, thereby achieving dynamic control of base station switching based on real-time network traffic demand and reducing network energy consumption.

[0038] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0039] The accompanying drawings, which form part of this specification, illustrate embodiments of this application and, together with the description, serve to explain the principles of this application.

[0040] This application can be more clearly understood with reference to the accompanying drawings and the following detailed description, wherein:

[0041] Figure 1 A schematic diagram of a base station dynamic control method for heterogeneous wireless networking provided in an embodiment of this application is shown;

[0042] Figure 2 This illustration shows a schematic diagram of a heterogeneous wireless network architecture provided in an embodiment of this application;

[0043] Figure 3 A flowchart illustrating a method for dynamic control of base stations in a heterogeneous wireless network according to an embodiment of this application is shown.

[0044] Figure 4 A flowchart illustrating another method for dynamic control of base stations in heterogeneous wireless networking provided in an embodiment of this application is shown.

[0045] Figure 5 This invention provides a schematic diagram of the structure of an electronic device according to an embodiment of the present application.

[0046] Figure 6 This illustration shows a schematic diagram of the structure of an electronic device according to an embodiment of this application;

[0047] Figure 7 A schematic diagram of a storage medium provided in one embodiment of this application is shown. Detailed Implementation

[0048] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0049] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0050] The following description of at least one exemplary embodiment is merely illustrative and is not intended to limit the scope of this application or its application or use.

[0051] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0052] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0053] Furthermore, the technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this application.

[0054] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0055] The following is combined Figures 1-4 This application describes a base station dynamic control method for heterogeneous wireless networking according to exemplary embodiments thereof. It should be noted that the following application scenarios are shown only to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way. Rather, the embodiments of this application can be applied to any applicable scenario.

[0056] This application also proposes a method, apparatus, electronic device, and medium for dynamic control of base stations in heterogeneous wireless networking.

[0057] Figure 1 A schematic flowchart illustrating a base station dynamic control method for a heterogeneous wireless network according to an embodiment of this application is shown. The heterogeneous wireless network includes a control base station providing data services in a first frequency band and a data base station providing data services in a second frequency band, wherein the second frequency band is higher than the first frequency band. Figure 1 As shown, the method includes:

[0058] S101, each data base station, based on the traffic prediction model and the mobility prediction model, predicts its own traffic demand in the future time period and then sends the traffic demand to the control base station.

[0059] S102, the control base station inputs the traffic demand of each data base station into a pre-built dynamic switching decision model to obtain a switching decision. The switching decision includes indication information for indicating whether at least one data base station provides data service.

[0060] S103, the control base station sends the switching decision to the data base station so that the data base station can determine whether to provide data service based on the indication information in the switching decision.

[0061] Among related technologies, high- and low-frequency heterogeneous wireless networking based on the separation of control base stations and data base stations can not only improve regional coverage capabilities, but also significantly improve the overall spectrum efficiency of the system, providing a seamless high-speed experience for access users. It is a key networking method considered by the 6th generation mobile communication system (6G).

[0062] The high- and low-frequency heterogeneous wireless networking primarily utilizes control base stations to achieve basic coverage across the entire area via low-frequency bands. It also leverages dense deployment of data base stations, using high-frequency bands for hotspot coverage and high-speed transmission. This aims to meet the demands of future 6G networks for higher data throughput, faster user experience rates, massive terminal connections, and lower latency. When data base stations detect a low number of users during certain periods, they can appropriately shut down some data base stations while maintaining user service quality, thus implementing dynamic cell switching. This reduces energy consumption and meets the green communication requirements of 6G development.

[0063] In one approach, to ensure wide-area coverage performance of the wireless network and provide flexible network function deployment, the 6G network will introduce a decoupling mechanism between control signaling (handled by the control base station) and service data (handled by the data base station). At the same time, to reduce network power consumption and cost issues caused by the dense deployment of high-frequency base station sites, the 6G network will also dynamically switch data base stations on and off.

[0064] One common technique in related technologies is to periodically shut down some base stations to avoid unnecessary energy consumption in the network, thereby alleviating network pressure. However, existing base station control methods are very fixed and cannot flexibly and intelligently control the activation and deactivation of base stations based on network conditions.

[0065] To address the aforementioned issues, this application proposes a technical solution whereby each data base station predicts its own traffic demand for the next moment based on the user's real-time mobile location and a traffic prediction model, and then feeds this prediction back to the control base station in a heterogeneous wireless network. This enables the control base station to adjust the operational status of one or more data base stations accordingly, based on the overall traffic demand of the data base stations in the network.

[0066] Specifically, Figure 2 The heterogeneous wireless network proposed in this application includes multiple data base stations communicating and multiple control base stations.

[0067] In one approach, within the service area covered by the control base station, a user can instruct the control base station to provide services related to control signaling transmission in a low-frequency band (i.e., the first frequency band). Conversely, within the service area covered by the data base station, a user can instruct the data base station to provide services related to data information transmission in a high-frequency period (i.e., the second frequency band).

[0068] Specifically, to reduce network power consumption and cost issues caused by dense deployment of high-frequency sites while ensuring wide-area network coverage, 6G networks will introduce a decoupling mechanism between control signaling and service data. Specifically, low-frequency band (i.e., the first band, for example, 700MHz) control base stations will provide wide-area unified signaling coverage, responsible for broadcasting control signaling such as RRC messages and physical layer signaling. This will reduce the impact of high path loss caused by high-frequency bands, ensuring continuous and reliable connectivity and mobility.

[0069] In addition, the heterogeneous wireless networking proposed in this application can also provide data transmission by high-capacity, on-demand high-frequency band (i.e., the second band, such as 62.5 GHz and above) data base stations, thereby achieving high-speed service data services and reducing the overall network energy consumption.

[0070] In one approach, the heterogeneous wireless networking proposed in this application can densely deploy data base stations in areas with high user access and high traffic demand to support a seamless high-speed user experience. In another approach, this application can also group data base stations that are geographically close and have overlapping coverage into the same heterogeneous wireless network to achieve centralized control of the data base stations through heterogeneous wireless networking.

[0071] In one approach, this application selects one data base station from multiple data base stations as the target data base station, enabling it to establish centralized backhaul link communication with the control base station. Other data base stations within the heterogeneous wireless network no longer need to establish backhaul link connections with the control base station; they can directly establish backhaul links with the target data base station within the heterogeneous wireless network to complete backhaul communication. This reduces the backhaul link burden on the control base station and lowers signaling overhead.

[0072] In addition, in the heterogeneous wireless network proposed in this application, the various data base stations within the heterogeneous wireless network can cooperate in processing by sharing data, channel state information (CSI), scheduling information, and precoding matrix index (PMI) information, so as to improve the performance of cell edge users.

[0073] In one approach, the target data base station has strong computing and storage capabilities, and the data and channel status information in the data cells within the heterogeneous wireless network are uniformly located. Therefore, controlling the heterogeneous wireless networking of adjacent data base stations can more easily achieve inter-cell interference coordination, thereby meeting the high experience rate requirements of users in hotspot areas.

[0074] In the high- and low-frequency heterogeneous wireless networking proposed in this application, a wireless link backhaul mode can be adopted. The wireless backhaul combined with millimeter-wave massive MIMO beamforming technology provides huge antenna gain for the wireless backhaul link, which can effectively combat the relatively high path loss caused by rainfall and air absorption.

[0075] Furthermore, in combination Figure 3 As shown below, the base station dynamic control method for heterogeneous wireless networking proposed in this application will be described in detail:

[0076] Step 1: The data base station sends a model distribution request to the control base station.

[0077] This requires each data base station in the heterogeneous wireless network to send a model distribution request to the corresponding control base station to obtain the initial traffic prediction model and the initial mobility prediction model.

[0078] Step 2: The data base station predicts traffic demand based on the received initial traffic prediction model and initial mobility prediction model.

[0079] As an example, in real-world applications, a user's daily or weekly wireless network traffic levels fluctuate periodically depending on their mobile lifestyle. For instance, during the daytime on weekdays, users tend to concentrate in urban commercial areas and are more likely to make phone calls. In the evenings or on weekends, users move to residential areas. This results in lower call frequencies at night compared to daytime, but with greater cellular data transmission because data-intensive applications such as social networks, web browsing, and video streaming are more likely to run. To address this tidal effect of wireless network traffic, embodiments of this application can adjust the level of over-provisioning activity by switching certain data base stations to off mode (or sleep mode, or low-power mode) to save energy without significantly impacting network traffic.

[0080] In one approach, network traffic prediction is essentially time series prediction for the traffic prediction model. In another approach, the traffic prediction for data base stations in this embodiment can be based on a time series model. The specific parameters of the model are solved using actual data, and finally, time series prediction is performed using the time series model with known parameters.

[0081] As an example, the traffic prediction model provided in this application can be a moving average (MA) model, an autoregressive (AR) model, an autoregressive moving average (ARMA) model, an autoregressive integrated moving average (ARIMA) model, a linear regression (LR) model, a support vector regression (SVR) model, a long short-term memory (LSTM) neural network, etc.

[0082] In addition, as location-based services are gaining increasing attention in real-world applications, collecting users' geographic location information can help to better target users and launch targeted services.

[0083] To address this, embodiments of this application can predict the future movement trajectory of each user based on their historical location information. It is understood that once a user's movement location is predicted, their location at a specific time can be obtained, thereby enabling resource planning or the provision of customized services.

[0084] In one approach, the mobility prediction model provided in this application can be a Markov Model (MM), a Hidden Markov Model (HMM), a Bayesian model, etc., which predicts the user's future location by calculating joint probabilities. Alternatively, it can be a Support Vector Machine (SVM), an Artificial Neural Network (ANN), a Deep Neural Network (DNN), or other models with more powerful learning capabilities.

[0085] In one approach, the data base station can directly perform traffic prediction based on the initial traffic prediction model and the initial mobility prediction model after receiving them.

[0086] In another approach, the data base station can also engage in federated learning with the control base station, iteratively uploading, aggregating (performed by the control base station), and then distributing the two prediction models back to the data base station until a better traffic prediction model and mobility prediction model are obtained. This improves network traffic prediction performance.

[0087] Specifically, for the upload step of federated learning, after receiving the initial traffic prediction model and the initial mobility prediction model, the data base station trains the initial traffic prediction model based on the locally stored historical network traffic dataset to obtain the trained and updated traffic prediction model, and then uploads the trained and updated traffic prediction model to the control base station.

[0088] In addition, the data base station also needs to train the initial mobility prediction model based on the historical mobility trajectory dataset stored locally to obtain the trained and updated mobility prediction model, and then upload the trained and updated mobility prediction model to the control base station.

[0089] Furthermore, the aggregation step in federated learning involves the control base station aggregating multiple trained and updated traffic prediction models sent by each data base station to obtain an aggregated traffic prediction model; and the control base station aggregating multiple trained and updated mobility prediction models sent by each data base station to obtain an aggregated mobility prediction model.

[0090] Furthermore, the re-distribution step in federated learning involves the control base station distributing the aggregated traffic prediction model and the aggregated mobility prediction model to each data base station. This allows each data base station to predict traffic demand based on these models.

[0091] The process of predicting traffic demand by data base stations may include:

[0092] First, data base stations can predict their initial traffic demand in the future based on traffic prediction models; and second, they can predict the location of users served by the data base stations in the future based on mobility prediction models.

[0093] In addition, the data base station can adjust the initial traffic demand based on the user's location and the user's reported traffic demand for future time periods in the past, thereby obtaining the data base station's own total traffic demand for future time periods.

[0094] In one approach, during the prediction of future traffic demand by the data base station in this embodiment, the impact of the constantly moving service demands of individual users in a heterogeneous wireless network on the future network traffic distribution is considered. Therefore, detailed characteristics at the individual user level can be captured, ultimately resulting in a more accurate and suitable traffic demand prediction for traffic load distribution.

[0095] Step 3: Each data base station sends its own traffic requirements to the control base station.

[0096] Step 4: The control base station inputs the traffic demand of each data base station into the pre-built dynamic switching decision model to obtain the switching decision.

[0097] The switching decision includes indication information for instructing at least one data base station whether to provide data services.

[0098] In one approach, the indication information in the switching decision can be used to instruct at least one data base station to temporarily stop providing data services, completely stop providing data services, start providing data services, provide data services in a high-power mode, provide data services in a low-power mode, and so on.

[0099] Understandably, after receiving traffic requests from multiple data base stations in a certain area, the control base station can adjust the operational status of one or more data base stations based on the overall traffic performance of the current network and the traffic demands of each data base station in future time periods.

[0100] For example, if redundant future network traffic is detected, at least one data base station in a specific cell can be shut down, or data base stations in a certain area can be adjusted to low-power operation. Alternatively, if insufficient future network traffic is detected to meet the needs of wireless terminals, all data base stations in the area (including at least one previously disabled data base station) can be activated, thereby improving the efficiency of service data processing.

[0101] Step 5: Control the base station to send the switching decision to each data base station in the heterogeneous wireless network; or, control the base station to send the switching decision to the first data base station in the heterogeneous wireless network.

[0102] Among them, the first data base station is the data base station that needs to stop or start providing data services, as reflected by the switching decision.

[0103] In one approach, the control base station can selectively send switching decisions to every or a subset of data base stations in the network based on its own load conditions. Understandably, these subsets of data base stations are those indicated in the switching decisions—data base stations that need to stop providing data services, data base stations that need to start providing data services, data base stations that need to adjust their power, and so on.

[0104] Step 6: Control the base station to send a broadcast notification to the wireless terminal providing data service, informing it that it needs to switch data transmission services with other data base stations.

[0105] Step 7: After determining that the wireless terminal has connected to another data base station, the first data base station controls itself to shut down.

[0106] In this application, each data base station can predict its own traffic demand in a future time period based on a traffic prediction model and a mobility prediction model, and then send the traffic demand to a control base station. The control base station inputs the traffic demand of each data base station into a pre-built dynamic switching decision model to obtain a switching decision. The switching decision includes indication information for indicating whether at least one data base station should provide data services. The control base station sends the switching decision to the data base stations so that the data base stations can determine whether to provide data services based on the indication information in the switching decision.

[0107] By applying the technical solution of this application, each data base station can predict its own traffic demand for the next moment based on the user's real-time mobile location and traffic prediction model, and then feed this prediction back to the control base station in the heterogeneous wireless network. This allows the control base station to adjust the operating status of one or more data base stations accordingly based on the overall traffic demand of the data base stations in the network. This achieves two goals: firstly, data base stations can incorporate user mobile location data to improve prediction accuracy during traffic forecasting; secondly, it ensures the globality and stability of regional network traffic distribution prediction, thereby dynamically controlling the switching on and off of data base stations based on the real-time traffic demand of the network and reducing network energy consumption.

[0108] Optionally, in another embodiment based on the method described above in this application, the prediction of one's own traffic demand in a future time period includes:

[0109] The data base station sends a model delivery request to the control base station, the model delivery request being used to request the control base station to send an initial traffic prediction model and an initial mobility prediction model;

[0110] The data base station predicts the traffic demand based on the received initial traffic prediction model and initial mobility prediction model.

[0111] Optionally, in another embodiment based on the method described above in this application, after the data base station sends a model distribution request to the control base station, the method further includes:

[0112] The data base station trains the initial traffic prediction model based on the locally stored historical network traffic dataset to obtain a trained and updated traffic prediction model; and the data base station trains the initial mobility prediction model based on the locally stored historical mobility trajectory dataset to obtain a trained and updated mobility prediction model.

[0113] After the data base station sends the trained and updated traffic prediction model and the trained and updated mobility prediction model to the control base station, the control base station stores the trained and updated traffic prediction model and the trained and updated mobility prediction model.

[0114] The data base station receives the aggregated traffic prediction model and the aggregated mobility prediction model sent by the control base station.

[0115] Optionally, in another embodiment based on the method described above in this application, after the data base station sends the trained and updated traffic prediction model and the trained and updated mobility prediction model to the control base station, the method further includes:

[0116] The control base station aggregates the trained and updated traffic prediction models sent by each data base station to obtain the aggregated traffic prediction model; and aggregates the trained and updated mobility prediction models sent by each data base station to obtain the aggregated mobility prediction model.

[0117] The control base station distributes the aggregated traffic prediction model and the aggregated mobility prediction model to each data base station;

[0118] The data base station predicts the traffic demand based on the aggregated traffic prediction model and the aggregated mobility prediction model.

[0119] Optionally, in another embodiment based on the method described above, each data base station predicts its own traffic demand in a future time period based on a traffic prediction model and a mobility prediction model, including:

[0120] The data base station predicts its initial traffic demand in the future time period based on the traffic prediction model; and predicts the user location of the users served by the data base station in the future time period based on the mobility prediction model.

[0121] Based on the user's location, the initial traffic demand is adjusted to obtain the user's traffic demand in the future time period.

[0122] Optionally, in another embodiment based on the method described above in this application, the control base station sends the switching decision to the data base station, including:

[0123] The control base station sends the switching decision to each data base station in the heterogeneous wireless network; or...

[0124] The control base station sends the switching decision to the first data base station in the heterogeneous wireless network. The first data base station is the data base station that needs to stop or start providing data services, as reflected by the switching decision.

[0125] Optionally, in another embodiment based on the method described above in this application, after the control base station sends the switching decision to the data base station, the method further includes:

[0126] The control base station sends a handover broadcast notification to the wireless terminal providing data services. The handover broadcast notification is used to inform the wireless terminal to perform data transmission services with other data base stations.

[0127] After determining that the wireless terminal has connected to the other data base station, the data base station controls itself to shut down.

[0128] like Figure 4 As shown, this application further illustrates the solution through another embodiment:

[0129] Step 1: The control base station in the heterogeneous wireless network sends measurement configuration information to the wireless terminal;

[0130] Step 2: The wireless terminal measures and obtains a configuration report, which includes its own current traffic information, location information, etc.

[0131] Step 3: The wireless terminal reports information such as traffic and location to its assigned data base station;

[0132] Step 4: The data base station sends a model delivery request to the control base station to request the initial models of the traffic prediction model and the mobility prediction model.

[0133] Step 5: Control the base station to send the initial models of the two models to each data base station;

[0134] Step 6: The data base station uses the network traffic prediction module to predict its own network traffic distribution for the next moment, based on the local historical network traffic dataset and the initial traffic prediction model.

[0135] The local historical network traffic dataset of the data base station can be derived from network traffic data collected at historical or real-time times for various time periods.

[0136] Step 7: The data base station uploads the updated traffic prediction model to the control base station for model storage;

[0137] In one approach, the interaction between the data base station and the control base station can utilize federated learning techniques. This involves iteratively uploading, aggregating, and redistributing the model until a better traffic prediction model is obtained. This ultimately improves network traffic prediction performance.

[0138] Step 8: The data base station uses the user mobility prediction module to predict the user's location at the next moment based on the local user trajectory dataset and the initial mobility prediction model;

[0139] Step 9: The data base station uploads the updated mobility prediction model to the control base station for model storage;

[0140] In one approach, the interaction between the data base station and the control base station model can also utilize federated learning techniques. This involves iteratively uploading, aggregating, and redeploying the model until a better mobility prediction model is obtained. This ultimately improves the performance of user mobility prediction.

[0141] Step 10: Based on the user location prediction results and the traffic demand reported by the user, the data base station estimates the data base station traffic for the next moment, and corrects the network traffic distribution for the next moment output by the traffic prediction module accordingly, so as to obtain the data base station's overall traffic demand for the future period and report it to the control base station.

[0142] Step 11: The control base station uses its own deployed data base station switch control module to combine the traffic demand reported by each data base station to obtain the switch decision scheme for each data base station in the next time period.

[0143] Step 12: The control base station sends the switching decision to each data base station;

[0144] Step 13: The control base station broadcasts the on / off configuration information of each data base station to the wireless terminal;

[0145] Step 14: The wireless terminal and the data base station complete the handover access according to the on / off configuration of the data base station in the next time period;

[0146] Step 15: The data base station is turned on / off according to the switch decision.

[0147] By applying the technical solution of this application, each data base station can predict its own traffic demand for the next moment based on the user's real-time mobile location and traffic prediction model, and then feed this prediction back to the control base station in the heterogeneous wireless network. This allows the control base station to adjust the operating status of one or more data base stations accordingly based on the overall traffic demand of the data base stations in the network. This achieves two goals: firstly, data base stations can incorporate user mobile location data to improve prediction accuracy during traffic forecasting; secondly, it ensures the globality and stability of traffic distribution prediction in the regional network, thereby enabling dynamic control of base station switching based on real-time network traffic demand and reducing network energy consumption.

[0148] Optionally, in another embodiment of this application, such as Figure 5 As shown, this application also provides a base station dynamic control device for heterogeneous wireless networking. The heterogeneous wireless networking includes a control base station providing data services in a first frequency band and a data base station providing data services in a second frequency band, wherein the second frequency band is higher than the first frequency band, wherein:

[0149] The prediction module 201 is configured such that each data base station, based on a traffic prediction model and a mobility prediction model, predicts its own traffic demand in a future time period and then sends the traffic demand to the control base station.

[0150] The generation module 202 is configured to input the traffic requirements of each data base station into a pre-built dynamic switching decision model to obtain a switching decision, wherein the switching decision includes indication information for indicating whether at least one data base station provides data services.

[0151] The control module 203 is configured such that the control base station sends the switching decision to the data base station, so that the data base station determines whether to provide data service based on the indication information in the switching decision.

[0152] By applying the technical solution of this application, each data base station can predict its own traffic demand for the next moment based on the user's real-time mobile location and traffic prediction model, and then feed this prediction back to the control base station in the heterogeneous wireless network. This allows the control base station to adjust the operating status of one or more data base stations accordingly based on the overall traffic demand of the data base stations in the network. This achieves two goals: firstly, data base stations can incorporate user mobile location data to improve prediction accuracy during traffic forecasting; secondly, it ensures the globality and stability of traffic distribution prediction in the regional network, thereby enabling dynamic control of base station switching based on real-time network traffic demand and reducing network energy consumption.

[0153] In another embodiment of this application, the control module 203 is configured to perform the following steps:

[0154] The data base station sends a model delivery request to the control base station, the model delivery request being used to request the control base station to send an initial traffic prediction model and an initial mobility prediction model;

[0155] The data base station predicts the traffic demand based on the received initial traffic prediction model and initial mobility prediction model.

[0156] In another embodiment of this application, the control module 203 is configured to perform the following steps:

[0157] The data base station trains the initial traffic prediction model based on the locally stored historical network traffic dataset to obtain a trained and updated traffic prediction model; and the data base station trains the initial mobility prediction model based on the locally stored historical mobility trajectory dataset to obtain a trained and updated mobility prediction model.

[0158] After the data base station sends the trained and updated traffic prediction model and the trained and updated mobility prediction model to the control base station, the control base station stores the trained and updated traffic prediction model and the trained and updated mobility prediction model.

[0159] The data base station receives the aggregated traffic prediction model and the aggregated mobility prediction model sent by the control base station.

[0160] In another embodiment of this application, the control module 203 is configured to perform the following steps:

[0161] The control base station aggregates the trained and updated traffic prediction models sent by each data base station to obtain the aggregated traffic prediction model; and aggregates the trained and updated mobility prediction models sent by each data base station to obtain the aggregated mobility prediction model.

[0162] The control base station distributes the aggregated traffic prediction model and the aggregated mobility prediction model to each data base station;

[0163] The data base station predicts the traffic demand based on the aggregated traffic prediction model and the aggregated mobility prediction model.

[0164] In another embodiment of this application, the control module 203 is configured to perform the following steps:

[0165] The data base station predicts its initial traffic demand in the future time period based on the traffic prediction model; and predicts the user location of the users served by the data base station in the future time period based on the mobility prediction model.

[0166] Based on the user's location, the initial traffic demand is adjusted to obtain the user's traffic demand in the future time period.

[0167] In another embodiment of this application, the control module 203 is configured to perform the following steps:

[0168] The control base station sends the switching decision to each data base station in the heterogeneous wireless network; or...

[0169] The control base station sends the switching decision to the first data base station in the heterogeneous wireless network. The first data base station is the data base station that needs to stop or start providing data services, as reflected by the switching decision.

[0170] In another embodiment of this application, the control module 203 is configured to perform the following steps:

[0171] The control base station sends a handover broadcast notification to the wireless terminal providing data services. The handover broadcast notification is used to inform the wireless terminal to perform data transmission services with other data base stations.

[0172] After determining that the wireless terminal has connected to the other data base station, the data base station controls itself to shut down.

[0173] This application also provides an electronic device for executing the above-described base station dynamic control method for heterogeneous wireless networking. Please refer to... Figure 6 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 6 As shown, the electronic device 3 includes: a processor 300, a memory 301, a bus 302, and a communication interface 303. The processor 300, the communication interface 303, and the memory 301 are connected through the bus 302. The memory 301 stores a computer program that can run on the processor 300. When the processor 300 runs the computer program, it executes the base station dynamic control method for heterogeneous wireless networking provided in any of the foregoing embodiments of this application.

[0174] The memory 301 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 303 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0175] Bus 302 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs. After receiving an execution instruction, the processor 300 executes the program. The data recognition method disclosed in any of the foregoing embodiments of this application can be applied to the processor 300, or implemented by the processor 300.

[0176] The processor 300 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 300 or by instructions in software form. The processor 300 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 301. The processor 300 reads the information in memory 301 and, in conjunction with its hardware, completes the steps of the above method.

[0177] The electronic device provided in this application embodiment and the base station dynamic control method for heterogeneous wireless networking provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0178] This application also provides a computer-readable storage medium corresponding to the base station dynamic control method for heterogeneous wireless networking provided in the foregoing embodiments. Please refer to... Figure 7 The computer-readable storage medium shown is an optical disc 40, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the base station dynamic control method for heterogeneous wireless networking provided in any of the foregoing embodiments.

[0179] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0180] The computer-readable storage medium provided in the above embodiments of this application and the data identification method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0181] It should be noted that:

[0182] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known structures and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0183] Similarly, it should be understood that, for the sake of brevity and to aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together in a single embodiment, figure, or description thereof. However, this disclosure should not be construed as reflecting a schematic diagram in which the claimed application requires more features than expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0184] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0185] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for dynamic control of base stations in heterogeneous wireless networking, characterized in that, The heterogeneous wireless network includes a control base station providing data services in a first frequency band and a data base station providing data services in a second frequency band, wherein the second frequency band is higher than the first frequency band, wherein: Each data base station, based on a traffic prediction model and a mobility prediction model, predicts its own traffic demand for a future time period and then sends the traffic demand to the control base station. The prediction of its own traffic demand for the future time period by each data base station includes: predicting its initial traffic demand for the future time period based on the traffic prediction model; and predicting the location of users served by the data base station for the future time period based on the mobility prediction model. Based on the user location, the initial traffic demand is corrected to obtain its own traffic demand for the future time period. The control base station inputs the traffic demand of each data base station into a pre-built dynamic switching decision model to obtain a switching decision. The switching decision includes indication information for indicating whether at least one data base station provides data services. The control base station sends the switching decision to the data base station, so that the data base station can determine whether to provide data services based on the indication information in the switching decision.

2. The method as described in claim 1, characterized in that, The prediction of its own traffic demand in the future time period includes: The data base station sends a model delivery request to the control base station, the model delivery request being used to request the control base station to send an initial traffic prediction model and an initial mobility prediction model; The data base station predicts the traffic demand based on the received initial traffic prediction model and initial mobility prediction model.

3. The method as described in claim 2, characterized in that, After the data base station sends a model distribution request to the control base station, the method further includes: The data base station trains the initial traffic prediction model based on the locally stored historical network traffic dataset to obtain a trained and updated traffic prediction model; and the data base station trains the initial mobility prediction model based on the locally stored historical mobility trajectory dataset to obtain a trained and updated mobility prediction model. After the data base station sends the trained and updated traffic prediction model and the trained and updated mobility prediction model to the control base station, the control base station stores the trained and updated traffic prediction model and the trained and updated mobility prediction model. The data base station receives the aggregated traffic prediction model and the aggregated mobility prediction model sent by the control base station.

4. The method as described in claim 3, characterized in that, After the data base station sends the trained and updated traffic prediction model and the trained and updated mobility prediction model to the control base station, the method further includes: The control base station aggregates the trained and updated traffic prediction models sent by each data base station to obtain the aggregated traffic prediction model; and aggregates the trained and updated mobility prediction models sent by each data base station to obtain the aggregated mobility prediction model. The control base station distributes the aggregated traffic prediction model and the aggregated mobility prediction model to each data base station; The data base station predicts the traffic demand based on the aggregated traffic prediction model and the aggregated mobility prediction model.

5. The method as described in claim 1, characterized in that, The control base station sends the switching decision to the data base station, including: The control base station sends the switching decision to each data base station in the heterogeneous wireless network; or... The control base station sends the switching decision to the first data base station in the heterogeneous wireless network. The first data base station is the data base station that needs to stop or start providing data services, as reflected by the switching decision.

6. The method as described in claim 5, characterized in that, After the control base station sends the switching decision to the data base station, the method further includes: The control base station sends a handover broadcast notification to the wireless terminal providing data services. The handover broadcast notification is used to inform the wireless terminal to perform data transmission services with other data base stations. After determining that the wireless terminal has connected to the other data base station, the data base station controls itself to shut down.

7. A base station dynamic control device for heterogeneous wireless networking, characterized in that, The heterogeneous wireless network includes a control base station providing data services in a first frequency band and a data base station providing data services in a second frequency band, wherein the second frequency band is higher than the first frequency band, wherein: The prediction module is configured such that each data base station, based on a traffic prediction model and a mobility prediction model, predicts its own traffic demand for a future time period and then sends the traffic demand to the control base station. The prediction of its own traffic demand for the future time period by each data base station based on the traffic prediction model and the mobility prediction model includes: predicting its own initial traffic demand for the future time period based on the traffic prediction model; and predicting the location of users served by the data base station for the future time period based on the mobility prediction model; and correcting the initial traffic demand based on the user location to obtain its own traffic demand for the future time period. The generation module is configured such that the control base station inputs the traffic requirements of each data base station into a pre-built dynamic switching decision model to obtain a switching decision, the switching decision including indication information for indicating whether at least one data base station provides data service; The control module is configured such that the control base station sends the switching decision to the data base station, so that the data base station determines whether to provide data services based on the indication information in the switching decision.

8. An electronic device, characterized in that, include: Memory, used to store executable instructions; as well as, A processor, configured to execute the executable instructions with the memory to perform the operation of the base station dynamic control method for heterogeneous wireless networking according to any one of claims 1-6.

9. A computer-readable storage medium for storing computer-readable instructions, characterized in that, When the instruction is executed, it performs the operation of the base station dynamic control method of any one of the heterogeneous wireless networking claims 1-6.

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