Resource reservation method, apparatus, and wireless network device

Abstract

The application relates to the technical field of communication, in particular to a resource reservation method, device and wireless network equipment. The method comprises the following steps: acquiring a wireless environment feature of an environment where the wireless network equipment is located in a target period, a network load condition of a wireless communication network provided by the wireless network equipment in the target period, and a resource scheduling strategy adopted by the wireless network equipment in the target period for the wireless environment feature and the network load condition; and determining communication resources reserved for the wireless communication network in a future period according to the wireless environment feature, the network load condition and the resource scheduling strategy, wherein the future period is later than the target period.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a resource reservation method, apparatus and wireless network equipment. Background Technology

[0002] In modern communication systems, wireless networks serve as a bridge connecting mobile devices and the core network, undertaking critical data transmission tasks. Wireless resource allocation is crucial in wireless networks, directly impacting network capacity, coverage, and the user's communication experience.

[0003] To address these challenges, resource reservation has become a key strategy for resolving bottlenecks in wireless resource allocation. By reserving a certain amount of bandwidth or spectrum resources for critical services or sudden traffic surges, the network can maintain stable operation under high load and burst conditions, ensuring service quality.

[0004] However, existing resource reservation methods typically rely on static or semi-dynamic configuration, which cannot adequately cope with the rapid changes in network demands, resulting in low resource utilization efficiency and an inability to flexibly adjust according to actual conditions. Summary of the Invention

[0005] Therefore, it is necessary to provide a resource reservation method, apparatus, and wireless network device that can optimize the resource reservation effect in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a resource reservation method, which includes:

[0007] The wireless environment characteristics of the wireless network device in the target time period, the network load of the wireless communication network provided by the wireless network device in the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load in the target time period are obtained.

[0008] Based on the characteristics of the wireless environment, network load, and resource scheduling strategies, determine the communication resources reserved for the wireless communication network in the future time period; wherein the future time period is later than the target time period.

[0009] In one embodiment, obtaining the resource scheduling strategy adopted by the wireless network device in response to wireless environment characteristics and network load conditions during a target time period includes:

[0010] Based on the modulation and coding scheme (MCS), transmission rank (RANK), and transmit power of the wireless network equipment, determine the communication configuration strategy adopted by the wireless network equipment in response to the characteristics of the wireless environment during the target time period.

[0011] Based on the amount of available resources provided by the wireless network device to the terminal devices connected to the wireless network during the target time period, and the data throughput between the wireless network and the terminal devices, determine the resource allocation strategy adopted by the wireless network device in the target time period according to the wireless environment characteristics and network load.

[0012] Based on the communication configuration strategy and resource allocation strategy, determine the resource scheduling characteristics adopted by the wireless network equipment in the target time period according to the wireless environment characteristics and network load.

[0013] In one embodiment, obtaining the wireless environment characteristics of the wireless network device's environment during a target time period includes:

[0014] Based on the signal strength RSRP of the wireless communication network during the target time period and the signal-to-interference-plus-noise ratio (SINR) fed back by the terminal devices connected to the wireless communication network during the target time period, the wireless environment characteristics of the environment where the wireless network devices are located during the target time period are determined.

[0015] In one embodiment, obtaining the network load of the wireless communication network provided by the wireless network device during a target time period includes:

[0016] The network load of the wireless communication network during the target time period is determined based on the number of connection requests (RRC) received by the wireless communication network during the target time period, the maximum number of connected users corresponding to the wireless communication network, and the average number of connected users of the wireless communication network.

[0017] In one embodiment, communication resources reserved for the wireless communication network in the future are determined based on wireless environment characteristics, network load, and resource scheduling strategies, including:

[0018] The wireless environment characteristics are input into the first analysis model to obtain the environmental impact characteristics;

[0019] Input the network load information into the second analysis model to obtain the load impact characteristics;

[0020] The resource scheduling strategy is input into the third analysis model to obtain the scheduling impact characteristics;

[0021] The network fusion characteristics are obtained by fusing the environmental impact characteristics, load impact characteristics, and scheduling impact characteristics.

[0022] By inputting network fusion features into the prediction model, the communication resources reserved for wireless communication networks in the future time period can be obtained.

[0023] In one embodiment, environmental impact characteristics, load impact characteristics, and scheduling impact characteristics are fused to obtain network fused characteristics, including:

[0024] The environmental impact features, load impact features, and scheduling impact features are input into the self-attention module to obtain the first feature weight corresponding to the environmental impact features, the second feature weight corresponding to the load impact features, and the third feature weight corresponding to the scheduling impact features.

[0025] The network fusion characteristics are determined by multiplying the environmental impact characteristics by the weight of the first characteristic, the load impact characteristics by the weight of the second characteristic, and the scheduling impact characteristics by the weight of the third characteristic.

[0026] Secondly, this application also provides a resource reservation device, comprising:

[0027] The acquisition module is used to acquire the wireless environment characteristics of the environment in which the wireless network device is located during the target time period, the network load of the wireless communication network provided by the wireless network device during the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load during the target time period.

[0028] The resource reservation module is used to determine the communication resources reserved for the wireless communication network in a future time period based on wireless environment characteristics, network load, and resource scheduling strategies; wherein the future time period is later than the target time period.

[0029] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0030] The wireless environment characteristics of the wireless network device in the target time period, the network load of the wireless communication network provided by the wireless network device in the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load in the target time period are obtained.

[0031] Based on the characteristics of the wireless environment, network load, and resource scheduling strategies, determine the communication resources reserved for the wireless communication network in the future time period; wherein the future time period is later than the target time period.

[0032] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0033] The wireless environment characteristics of the wireless network device in the target time period, the network load of the wireless communication network provided by the wireless network device in the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load in the target time period are obtained.

[0034] Based on the characteristics of the wireless environment, network load, and resource scheduling strategies, determine the communication resources reserved for the wireless communication network in the future time period; wherein the future time period is later than the target time period.

[0035] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0036] The wireless environment characteristics of the wireless network device in the target time period, the network load of the wireless communication network provided by the wireless network device in the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load in the target time period are obtained.

[0037] Based on the characteristics of the wireless environment, network load, and resource scheduling strategies, determine the communication resources reserved for the wireless communication network in the future time period; wherein the future time period is later than the target time period.

[0038] The aforementioned resource reservation method, apparatus, and wireless network equipment comprehensively consider multiple dimensions of information, including wireless environment characteristics, network load, and resource scheduling strategies. Employing automated and intelligent analysis methods, they capture the changing trends of wireless network resource demand within a target time period. This technical solution can accurately predict future resource demands. By dynamically adjusting the resource reservation ratio, this solution can utilize wireless resources more effectively. This helps avoid excessive waste and shortage of resources, thereby improving resource utilization efficiency. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a diagram illustrating the application environment of a resource reservation method in one embodiment.

[0041] Figure 2 This is a flowchart illustrating a resource reservation method in one embodiment;

[0042] Figure 3 This is a flowchart illustrating the steps of obtaining the resource scheduling strategy adopted by a wireless network device in a target time period based on wireless environment characteristics and network load conditions, as shown in one embodiment.

[0043] Figure 4This is a flowchart illustrating the steps for determining communication resources reserved for a wireless communication network in a future time period, as shown in one embodiment.

[0044] Figure 5 This is a flowchart illustrating the steps for obtaining network fusion features in one embodiment;

[0045] Figure 6 This is a schematic diagram of the network model corresponding to the resource reservation method in another embodiment;

[0046] Figure 7 This is a structural block diagram of a resource reservation device in one embodiment;

[0047] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] The resource reservation method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with wireless network device 104 via a network.

[0050] In one exemplary embodiment, such as Figure 2 As shown, a resource reservation method is provided, which can be applied to... Figure 1 The following explanation uses wireless network device 104 as an example:

[0051] S201, obtain the wireless environment characteristics of the environment where the wireless network device is located during the target time period, the network load of the wireless communication network provided by the wireless network device during the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load during the target time period.

[0052] Wireless environment characteristics refer to the comprehensive performance of factors such as radio wave propagation conditions, interference, and signal coverage in the environment in which wireless network equipment is located. These characteristics directly affect the performance and quality of wireless communication networks.

[0053] For example, in an indoor wireless environment, wireless environment characteristics may include the shielding effect of buildings, the influence of indoor furnishings, and the loss due to floor separation. In a vehicle-mounted wireless environment, wireless environment characteristics may include the vehicle's speed and the fact that the average power of the received signal decreases exponentially with increasing distance.

[0054] Optionally, methods for obtaining wireless environment characteristics can indeed include both direct acquisition and indirect acquisition through signal quality. The following are specific explanations of these two methods: 1) Directly measuring the wireless environment using specialized radio measurement equipment, such as spectrum analyzers and signal strength testers. These devices can accurately acquire various parameters of the wireless environment, such as signal strength, noise level, and channel occupancy. 2) Indirectly inferring the characteristics of the wireless environment by monitoring the signal quality indicators of wireless communication devices, such as bit error rate, packet loss rate, and latency. For example, a high bit error rate may indicate strong interference or signal attenuation in the wireless environment.

[0055] Network load refers to the data transmission pressure and workload that wireless network devices experience within a specific time period. It directly reflects the network's operational status. The magnitude of network load directly affects the network's response speed, data transmission efficiency, and stability.

[0056] For example, when the network load is light, devices in the network can easily handle the transmitted data, with fast response times and high data transmission efficiency. As the network load increases, the amount of data that devices in the network need to process increases, which may lead to longer response times, reduced data transmission efficiency, and even network congestion and failures.

[0057] Resource scheduling strategies refer to the dynamic adjustment and optimization of resource allocation and usage in wireless communication networks by wireless network devices based on wireless environment characteristics and network load conditions, in order to improve network performance and quality. These resources may include spectrum, power, antennas, and bandwidth. The goal of resource scheduling strategies is to ensure that, with limited communication resources, user communication needs are met using the least amount of resources, while simultaneously improving network capacity, throughput, and user experience.

[0058] For example, in dynamic resource optimization, wireless network devices can use strategies such as dynamic power control, dynamic spectrum allocation and sharing to adjust the allocation and use of network resources in real time based on real-time information such as network load and environmental changes, in order to adapt to different user needs and network conditions.

[0059] Optionally, the resource scheduling strategy is obtained by using the uplink modulation and coding scheme (MCS), physical resource block (PRBk), transmission rank (RANK), and transmit power as model inputs, and then employing a neural network to extract information such as the wireless resource scheduling strategy to be adopted for the current wireless environment. This information reflects the resource utilization and the effectiveness of the scheduling strategy in the current network environment, thus providing a basis for subsequent resource reservation and scheduling decisions.

[0060] S202, based on wireless environment characteristics, network load and resource scheduling strategy, determine the communication resources reserved for the wireless communication network in the future period.

[0061] The future time period is later than the target time period.

[0062] Understandably, wireless environment characteristics typically include features such as RSRP (Reference Signal Received Power) and SINR (Signal-to-Interference-plus-Noise Ratio). These data reflect the current quality of the wireless environment and form the basis for predictive models to assess future resource needs.

[0063] Network load data includes network load conditions and resource scheduling strategies, such as uplink PDCP throughput, RRC connection request count, maximum number of RRC users, and average number of RRC users. This data provides detailed information about current network usage, helping models predict future network load.

[0064] Resource scheduling strategies are extracted by neural networks from inputs such as uplink MCS, PRB, RANK, and transmit power. These strategies reflect how the network allocates and manages resources and are important references for predicting future resource demands.

[0065] Optionally, wireless environment characteristics, network load conditions, and resource scheduling strategies can be input into the prediction model to obtain the communication resources reserved for the wireless communication network in the future time period output by the prediction model.

[0066] Specifically, the aforementioned wireless environment characteristics, network load, and resource scheduling strategies are taken as input and fed into a trained prediction model, which is an RNN model. RNN models are suitable for processing sequential data and can capture dependencies in time series data, which is crucial for predicting future resource demands. The RNN model uses its hidden states to pass information from the previous time step to the next, thus capturing the temporal dependencies in the sequential data. This allows the model to predict future network conditions and resource demands based on historical data. After processing by the RNN model, the output is the communication resources reserved for the wireless communication network in the future time period.

[0067] The resource reservation method described above comprehensively considers information from multiple dimensions, including wireless environment characteristics, network load, and resource scheduling strategies. It employs automated and intelligent analysis methods to capture the changing trends of wireless network resource demand within a target time period. This technical solution can accurately predict future resource needs. By dynamically adjusting the resource reservation ratio, this solution can utilize wireless resources more effectively. This helps avoid excessive waste and shortage of resources, thereby improving resource utilization efficiency.

[0068] In one exemplary embodiment, such as Figure 3 As shown, the resource scheduling strategy adopted by the wireless network device during the target time period based on the wireless environment characteristics and network load includes:

[0069] S301, based on the modulation and coding scheme (MCS), transmission rank (RANK), and transmit power of the wireless network device, determine the communication configuration strategy adopted by the wireless network device in response to the characteristics of the wireless environment during the target time period.

[0070] Modulation and Coding Strategy (MCS): The MCS determines the modulation method and coding rate of data transmission, directly affecting the reliability and rate of data transmission. Different MCS levels correspond to different modulation and coding methods to adapt to different wireless environment characteristics (such as signal strength, interference level, etc.).

[0071] Transmission Rank (RANK): In multi-antenna transmission technology, RANK represents the number of parallel data streams transmitted. It reflects the ability of wireless network devices to improve data transmission rates using multi-antenna technology. The choice of transmission rank depends on the spatial characteristics of the wireless environment, such as the rank of the channel matrix.

[0072] Transmit power: Transmit power determines the strength of the signal transmitted by a wireless network device. In wireless communication, transmit power is one of the key factors affecting signal coverage and reception quality. By adjusting the transmit power, wireless network devices can adapt to different wireless environment characteristics, ensuring reliable signal transmission.

[0073] Taking all the above factors into account, wireless network devices can select appropriate MCS level, transmission rank, and transmit power based on the current wireless environment characteristics (such as signal quality and interference level), thereby determining the communication configuration strategy within the target time period.

[0074] S302, based on the amount of available resources provided by the wireless network device to the terminal devices connected to the wireless communication network during the target time period, and the data throughput between the wireless communication network and the terminal devices, determine the resource allocation strategy adopted by the wireless network device in the target time period according to the wireless environment characteristics and network load.

[0075] Available resources: This usually refers to the number of physical resource blocks (PRBs) or other forms of resources (such as time, frequency, etc.) that a wireless network device can allocate to terminal devices during a target time period.

[0076] Data throughput: Data throughput reflects the rate and efficiency of data transmission between a wireless communication network and terminal devices. It depends on various factors, including wireless environment characteristics, network load, and the performance of the terminal devices.

[0077] Wireless network devices need to allocate available resources reasonably based on the current wireless environment characteristics, network load, and the actual needs of terminal devices (such as data throughput requirements). This includes determining how many resources should be allocated to each terminal device, when to allocate resources, and how to allocate resources.

[0078] S303, based on the communication configuration strategy and resource allocation strategy, determines the resource scheduling characteristics adopted by the wireless network device in the target time period according to the wireless environment characteristics and network load.

[0079] In this embodiment, by integrating communication configuration strategies and resource allocation strategies, the wireless network device can form a complete resource scheduling scheme, which not only takes into account the current wireless environment characteristics and network load, but also the actual needs and performance limitations of the terminal device.

[0080] In an exemplary embodiment, obtaining the wireless environment characteristics of the environment where the wireless network device is located during a target time period includes: determining the wireless environment characteristics of the environment where the wireless network device is located during the target time period based on the signal strength RSRP of the wireless communication network during the target time period and the signal-to-interference-plus-noise ratio (SINR) fed back by the terminal device connected to the wireless communication network during the target time period.

[0081] RSRP (Reference Signal Received Power): RSRP refers to the power of the reference signal received by a terminal device from a wireless network device. RSRP is an important indicator of wireless signal coverage strength. A higher RSRP value indicates stronger signal coverage and better communication quality between the terminal device and the wireless network device. Wireless communication networks periodically or in real time measure and report RSRP values ​​so that wireless network devices can understand the current signal coverage situation.

[0082] Signal-to-Interference-plus-Noise Ratio (SINR): SINR is the ratio of the power of the useful signal received by a terminal device to the sum of the power of the received interference signal and noise. SINR is a key indicator of wireless communication link quality. A higher SINR value indicates that the useful signal has a greater advantage over interference and noise, resulting in better communication quality. Terminal devices periodically or in real-time report their received SINR values ​​to the wireless network equipment so that the wireless network equipment can understand the current communication link quality.

[0083] After obtaining RSRP and SINR, wireless network devices can comprehensively judge the wireless environment characteristics of the target time period based on the values ​​of these two parameters. For example, if both RSRP and SINR are high, it indicates that the wireless network device is in an environment with strong signal coverage and good communication link quality. In this case, the resource reservation ratio can be appropriately reduced to make full use of network resources.

[0084] If the RSRP or SINR value is low, it indicates that the wireless network device is located in an environment with weak signal coverage or poor communication link quality. In this case, it is necessary to increase the resource reservation ratio to ensure that stable service can be provided during sudden traffic surges or critical business demands.

[0085] In this embodiment, by acquiring the signal strength RSRP of the wireless communication network during the target time period and the signal-to-interference-plus-noise ratio (SINR) fed back by the terminal device, the wireless network device can accurately understand the wireless environment characteristics of its surroundings.

[0086] In an exemplary embodiment, obtaining the network load of the wireless communication network provided by the wireless network device during a target time period includes: determining the network load of the wireless communication network during the target time period based on the number of connection requests (RRC) received by the wireless communication network during the target time period, the maximum number of connected users corresponding to the wireless communication network, and the average number of connected users of the wireless communication network.

[0087] Radio Resource Control (RRC) connection request count: The RRC connection request count refers to the number of connection requests received by the wireless communication network from terminal devices within a target time period. The RRC connection request count is an important indicator of network load. A higher RRC connection request count indicates that the network is under heavy load and requires more resources to handle the connection requests. The wireless communication network records and analyzes the RRC connection request count in real time or periodically so that wireless network devices can understand the current network load.

[0088] Maximum Connected Users (MDP): MDP refers to the maximum number of concurrent users a wireless communication network can support. It is an important indicator of network capacity and load capacity. Understanding MDP helps wireless network devices avoid exceeding network capacity during resource allocation, which could lead to a decline in service quality. MDP is typically determined based on the planning and configuration of the wireless communication network and is tested and verified during network construction or upgrades.

[0089] Average Connected Users (ATUs): AATUs refer to the average number of concurrent users supported by a wireless communication network during a target time period. AATUs are an important indicator for measuring the actual network load. By comparing AATUs with the maximum number of concurrent users, we can understand the network utilization and load level. Wireless communication networks will collect AATUs statistics in real time or periodically so that wireless network devices can understand the current network load.

[0090] After obtaining the number of RRC connection requests, the maximum number of connected users, and the average number of connected users, the wireless network device can comprehensively judge the network load situation for the target time period based on the values ​​of these three parameters. For example, if the number of RRC connection requests is high and the average number of connected users is close to or reaches the maximum number of connected users, it indicates that the network is under high load, and the resource reservation ratio needs to be increased to cope with possible network congestion. If the number of RRC connection requests is low and the average number of connected users is much lower than the maximum number of connected users, it indicates that the network is under low load, and the resource reservation ratio can be appropriately reduced to make full use of network resources.

[0091] In one exemplary embodiment, such as Figure 4 As shown, based on wireless environment characteristics, network load, and resource scheduling strategies, the communication resources reserved for the wireless communication network in the future time period are determined, including:

[0092] S401: Input the wireless environment characteristics into the first analysis model to obtain the environmental impact characteristics.

[0093] Optionally, wireless environment features primarily reflect the physical characteristics of the environment in which the wireless communication network operates, such as signal strength and signal quality. These features are crucial for predicting future network performance and resource requirements. The first analytical model selected is a CNN model, which can automatically learn and extract useful features from the input data. By inputting the wireless environment features into the CNN model, environmental impact features reflecting the influence of the environment on network performance can be obtained.

[0094] S402, input the network load information into the second analysis model to obtain the load impact characteristics.

[0095] Optionally, network load primarily reflects the current load status of the wireless communication network, such as the number of connection requests and the number of connected users. This information is crucial for understanding network congestion and resource utilization. Similarly, a CNN model was selected as the second analysis model. By inputting network load data into the CNN model, load impact characteristics reflecting the influence of network load on network performance can be obtained.

[0096] S403, input the resource scheduling strategy into the third analysis model to obtain the scheduling impact characteristics.

[0097] Optionally, the resource scheduling strategy primarily reflects the current resource allocation and scheduling status of the wireless communication network. Different resource scheduling strategies will have different impacts on network performance and resource requirements. The third analysis model was also selected as a CNN model. By inputting the resource scheduling strategy into the CNN model, scheduling impact characteristics reflecting the impact of the scheduling strategy on network performance can be obtained.

[0098] S404 performs feature fusion on environmental impact characteristics, load impact characteristics, and scheduling impact characteristics to obtain network fusion characteristics.

[0099] After obtaining the environmental impact characteristics, load impact characteristics, and scheduling impact characteristics, these characteristics need to be fused. The purpose of feature fusion is to integrate feature information from different sources to form a more comprehensive and accurate network fusion feature. The network fusion feature integrates information from multiple aspects such as wireless environment, network load, and resource scheduling, and can more accurately reflect the resource demand of wireless communication networks in the future.

[0100] Optional, such as Figure 5 As shown, feature fusion is performed on environmental impact characteristics, load impact characteristics, and scheduling impact characteristics to obtain network fusion characteristics, including:

[0101] S501, input the environmental impact features, load impact features and scheduling impact features into the self-attention module to obtain the first feature weight corresponding to the environmental impact features, the second feature weight corresponding to the load impact features, and the third feature weight corresponding to the scheduling impact features.

[0102] Self-attention is a feature fusion method that calculates the correlation between input features and assigns a weight to each feature accordingly. This weight reflects the importance of the feature in the fusion process. By inputting environmental impact features, load impact features, and scheduling impact features into the self-attention module, the weights corresponding to each feature can be obtained: the first feature weight (corresponding to the environmental impact feature), the second feature weight (corresponding to the load impact feature), and the third feature weight (corresponding to the scheduling impact feature).

[0103] S502, determine the network fusion characteristics based on the product of environmental impact characteristics and the weight of the first characteristic, the product of load impact characteristics and the weight of the second characteristic, and the product of scheduling impact characteristics and the weight of the third characteristic.

[0104] After obtaining the weight corresponding to each feature, each feature is multiplied by its corresponding weight, and the results are summed to obtain the network fusion feature. This process achieves effective integration of information from different features. The network fusion feature integrates information from multiple aspects such as environmental impact features, load impact features, and scheduling impact features, and can more accurately reflect the resource demand of wireless communication networks in the future.

[0105] S503 inputs network fusion features into the prediction model to obtain the communication resources reserved for wireless communication networks in the future time period.

[0106] Optionally, the prediction model is a deep learning-based model that learns future trends in resource demand from input features. In this step, network fusion features are used as input to the prediction model. Through the calculations of the prediction model, the amount of communication resources reserved for the wireless communication network in the future can be obtained. This resource reservation strategy can utilize wireless resources more effectively, improving network stability and user communication experience.

[0107] In one exemplary embodiment, such as Figure 6 As shown, a network model corresponding to the aforementioned resource reservation method is provided. This model employs a multi-head mechanism, using three categories of information (wireless environment features, network load, and resource scheduling strategy) as inputs to three sub-CNN neural networks. These three types of information are processed separately, independently learning the specific features of each category of data. In the feature fusion stage, a self-attention mechanism is introduced, dynamically adjusting the proportion of each feature in the fusion process by calculating the weight of each feature in resource reservation. This model can combine multi-dimensional information from the wireless environment, resource scheduling, and network load to generate a more accurate resource reservation strategy.

[0108] Then, the RNN (Recurrent Neural Network) module processes the time-series data, primarily capturing historical network parameter metrics, rates, and RB (Resource Block) resource occupancy information to obtain future resource predictions. This allows for the generation of effective resource reservation strategies to ensure the target rate. By inputting effective features extracted by the CNN, the output of this RNN module is the prediction of future resource demand.

[0109] Finally, based on the forecast of future resource demand, an effective resource reservation strategy is generated. The resource reservation ratio is dynamically adjusted according to changes in future resource demand, thereby ensuring user speed and improving user experience.

[0110] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0111] Based on the same inventive concept, this application also provides a resource reservation device for implementing the resource reservation method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more resource reservation device embodiments provided below can be found in the limitations of the resource reservation method described above, and will not be repeated here.

[0112] In one exemplary embodiment, such as Figure 7 As shown, a resource reservation device is provided, including: an acquisition module 11 and a resource reservation module 12, wherein:

[0113] The acquisition module 11 is used to acquire the wireless environment characteristics of the environment in which the wireless network device is located during the target time period, the network load of the wireless communication network provided by the wireless network device during the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load during the target time period.

[0114] The resource reservation module 12 is used to determine the communication resources reserved for the wireless communication network in a future time period based on the characteristics of the wireless environment, network load, and resource scheduling strategy; wherein the future time period is later than the target time period.

[0115] In one embodiment, the acquisition module 11 is further configured to: acquire the resource scheduling strategy adopted by the wireless network device in response to wireless environment characteristics and network load conditions during a target time period, including:

[0116] Based on the modulation and coding scheme (MCS), transmission rank (RANK), and transmit power of the wireless network equipment, determine the communication configuration strategy adopted by the wireless network equipment in response to the characteristics of the wireless environment during the target time period.

[0117] Based on the amount of available resources provided by the wireless network device to the terminal devices connected to the wireless network during the target time period, and the data throughput between the wireless network and the terminal devices, determine the resource allocation strategy adopted by the wireless network device in the target time period according to the wireless environment characteristics and network load.

[0118] Based on the communication configuration strategy and resource allocation strategy, determine the resource scheduling characteristics adopted by the wireless network equipment in the target time period according to the wireless environment characteristics and network load.

[0119] In one embodiment, the acquisition module 11 is further configured to: determine the wireless environment characteristics of the wireless network device during the target time period based on the signal strength RSRP of the wireless communication network during the target time period and the signal-to-interference-plus-noise ratio (SINR) fed back by the terminal device connected to the wireless communication network during the target time period.

[0120] In one embodiment, the acquisition module 11 is further configured to: determine the network load of the wireless communication network during the target time period based on the number of connection requests (RRC) received by the wireless communication network during the target time period, the maximum number of connected users corresponding to the wireless communication network, and the average number of connected users of the wireless communication network.

[0121] In one embodiment, the resource reservation module 12 is further configured to: input wireless environment characteristics into the first analysis model to obtain environmental impact characteristics;

[0122] Input the network load information into the second analysis model to obtain the load impact characteristics;

[0123] The resource scheduling strategy is input into the third analysis model to obtain the scheduling impact characteristics;

[0124] The network fusion characteristics are obtained by fusing the environmental impact characteristics, load impact characteristics, and scheduling impact characteristics.

[0125] By inputting network fusion features into the prediction model, the communication resources reserved for wireless communication networks in the future time period can be obtained.

[0126] In one embodiment, the resource reservation module 12 is further configured to: input the environmental impact features, load impact features and scheduling impact features into the self-attention module to obtain the first feature weight corresponding to the environmental impact features, the second feature weight corresponding to the load impact features, and the third feature weight corresponding to the scheduling impact features.

[0127] The network fusion characteristics are determined by multiplying the environmental impact characteristics by the weight of the first characteristic, the load impact characteristics by the weight of the second characteristic, and the scheduling impact characteristics by the weight of the third characteristic.

[0128] Each module in the aforementioned resource reservation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0129] In one exemplary embodiment, a computer device is provided, which may be a wireless network device, and its internal structure diagram may be as follows. Figure 8 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores XX data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a resource reservation method.

[0130] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0131] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0132] The wireless environment characteristics of the wireless network device in the target time period, the network load of the wireless communication network provided by the wireless network device in the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load in the target time period are obtained.

[0133] Based on the characteristics of the wireless environment, network load, and resource scheduling strategies, determine the communication resources reserved for the wireless communication network in the future time period; wherein the future time period is later than the target time period.

[0134] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0135] The wireless environment characteristics of the wireless network device in the target time period, the network load of the wireless communication network provided by the wireless network device in the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load in the target time period are obtained.

[0136] Based on the characteristics of the wireless environment, network load, and resource scheduling strategies, determine the communication resources reserved for the wireless communication network in the future time period; wherein the future time period is later than the target time period.

[0137] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0138] The wireless environment characteristics of the wireless network device in the target time period, the network load of the wireless communication network provided by the wireless network device in the target time period, and the resource scheduling strategy adopted by the wireless network device in response to the wireless environment characteristics and network load in the target time period are obtained.

[0139] Based on the characteristics of the wireless environment, network load, and resource scheduling strategies, determine the communication resources reserved for the wireless communication network in the future time period; wherein the future time period is later than the target time period.

[0140] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0141] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0142] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A resource reservation method, characterized in that, Applied to wireless network devices, the method includes: The wireless environment characteristics of the environment where the wireless network device is located during the target time period and the network load of the wireless communication network provided by the wireless network device during the target time period are obtained; the wireless environment characteristics are determined based on the radio wave propagation conditions, interference conditions and signal coverage of the environment where the wireless network device is located. Based on the modulation and coding scheme (MCS), transmission rank (RANK), and transmit power of the wireless network device, a communication configuration strategy for the wireless network device in the target time period, considering the wireless environment characteristics, is determined. Based on the available resources provided by the wireless network device to the terminal devices connected to the wireless communication network in the target time period, and the data throughput between the wireless communication network and the terminal devices, a resource allocation strategy for the wireless network device in the target time period, considering the wireless environment characteristics and the network load, is determined. Based on the communication configuration strategy and the resource allocation strategy, a resource scheduling strategy for the wireless network device in the target time period, considering the wireless environment characteristics and the network load, is determined. The wireless environment characteristics are input into a first analysis model to obtain environmental impact characteristics; the network load is input into a second analysis model to obtain load impact characteristics; the resource scheduling strategy is input into a third analysis model to obtain scheduling impact characteristics; the environmental impact characteristics, load impact characteristics, and scheduling impact characteristics are fused to obtain network fusion characteristics; the network fusion characteristics are input into a prediction model to obtain communication resources reserved for the wireless communication network in the future time period; wherein, the prediction model predicts the reserved resources based on the temporal dependencies in the network fusion characteristics, and the future time period is later than the target time period.

2. The method according to claim 1, characterized in that, Obtaining the wireless environment characteristics of the wireless network device's environment during the target time period includes: Based on the signal strength RSRP of the wireless communication network during the target time period and the signal-to-interference-plus-noise ratio (SINR) fed back by the terminal devices connected to the wireless communication network during the target time period, the wireless environment characteristics of the environment in which the wireless network device is located during the target time period are determined.

3. The method according to claim 1, characterized in that, Obtaining the network load of the wireless communication network provided by the wireless network device during the target time period includes: The network load of the wireless communication network during the target time period is determined based on the number of connection requests (RRC) received by the wireless communication network during the target time period, the maximum number of connected users corresponding to the wireless communication network, and the average number of connected users of the wireless communication network.

4. The method according to claim 1, characterized in that, The feature fusion of the environmental impact features, the load impact features, and the scheduling impact features to obtain network fusion features includes: The environmental impact feature, the load impact feature, and the scheduling impact feature are input into the self-attention module to obtain the first feature weight corresponding to the environmental impact feature, the second feature weight corresponding to the load impact feature, and the third feature weight corresponding to the scheduling impact feature. The network fusion features are determined based on the product of the environmental impact feature and the first feature weight, the product of the load impact feature and the second feature weight, and the product of the scheduling impact feature and the third feature weight.

5. The method according to claim 4, characterized in that, The step of determining network fusion features based on the product of the environmental impact feature and the first feature weight, the product of the load impact feature and the second feature weight, and the product of the scheduling impact feature and the third feature weight includes: Determine the first product of the environmental impact feature and the first feature weight, the second product of the load impact feature and the second feature weight, and the third product of the scheduling impact feature and the third feature weight; The network fusion feature is obtained by summing the first product, the second product, and the third product.

6. The method according to claim 1, characterized in that, Obtaining the wireless environment characteristics of the wireless network device's environment during the target time period includes: By using radio measurement equipment to measure the environment in which the wireless network device is located, the wireless environment characteristics of the environment in which the wireless network device is located are obtained during the target time period; or... Based on the monitored signal quality indicators of the wireless network device, the wireless environment characteristics of the environment in which the wireless network device is located during the target time period are determined.

7. A resource reservation device, characterized in that, Configured in a wireless network device, the device includes: The acquisition module is used to acquire the wireless environment characteristics of the environment in which the wireless network device is located during a target time period, and the network load of the wireless communication network provided by the wireless network device during the target time period; determine the communication configuration strategy adopted by the wireless network device in the target time period in response to the wireless environment characteristics based on the modulation and coding scheme (MCS), transmission rank (RANK), and transmit power of the wireless network device; determine the resource allocation strategy adopted by the wireless network device in the target time period in response to the wireless environment characteristics and the network load based on the available resources provided by the wireless network device for the terminal devices connected to the wireless communication network during the target time period, and the data throughput between the wireless communication network and the terminal devices; and determine the resource scheduling strategy adopted by the wireless network device in the target time period in response to the wireless environment characteristics and the network load based on the communication configuration strategy and the resource allocation strategy; the wireless environment characteristics are determined based on the radio wave propagation conditions, interference conditions, and signal coverage of the environment in which the wireless network device is located. The resource reservation module is used to input the wireless environment characteristics into a first analysis model to obtain environmental impact characteristics; input the network load status into a second analysis model to obtain load impact characteristics; input the resource scheduling strategy into a third analysis model to obtain scheduling impact characteristics; perform feature fusion on the environmental impact characteristics, the load impact characteristics, and the scheduling impact characteristics to obtain network fusion characteristics; input the network fusion characteristics into a prediction model to obtain communication resources reserved for the wireless communication network in a future time period; wherein, the prediction model predicts the reserved resources based on the temporal dependencies in the network fusion characteristics, and the future time period is later than the target time period.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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