Wireless network communication method and system

Abstract

The present invention relates to a wireless network communication method and system, and belongs to the field of communication technology. The method includes: integrating channel monitoring, data acquisition and preliminary processing functions to realize real-time perception of the environment and data preprocessing; integrating adaptive power control and resource allocation functions to realize dynamic adjustment of transmission power and intelligent allocation of network resources through cross-level optimization decisions; utilizing edge computing capabilities to share system architecture optimization tasks, dynamically scheduling computing and communication resources; using virtualization technology to dynamically configure resources and centrally manage the overall architecture of the system. The invention significantly improves the energy efficiency and communication quality of the system through the comprehensive application of cross-level collaborative optimization, adaptive power control and edge computing technologies, and realizes efficient utilization of resources and flexible management of network architecture.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a wireless network communication method and system. Background Art

[0002] With the rapid development and widespread application of wireless communication technology, the surge in the number of mobile devices and the explosive growth in data demand have brought unprecedented challenges to wireless networks. Traditional wireless network communication methods are difficult to effectively manage limited communication resources in the face of complex and changing network environments, resulting in reduced network performance, increased energy consumption, and poor user experience.

[0003] Current wireless communication systems usually use fixed power control and resource allocation strategies, which lack the ability to dynamically respond to real-time channel status, changes in user demand, and fluctuations in network load. As a result, it is impossible to achieve an optimal balance between communication quality and energy efficiency, especially under high load and complex environments, where the shortcomings of traditional methods are particularly prominent. In addition, with the rise of edge computing and virtualization technologies, how to combine these technologies with wireless network resource management to improve resource utilization and system flexibility has become an urgent problem to be solved.

[0004] To meet the above challenges, there is an urgent need for a dynamic wireless communication method and system that can comprehensively consider channel status, user needs and network load to achieve optimal resource allocation and power control, and improve the overall energy efficiency and communication quality of the system. This method not only requires power control at the physical level, but also requires coordinated optimization of resource allocation at the network level, and can flexibly adapt to changing network environments and user needs. Summary of the invention

[0005] The present invention provides a wireless network communication method and system to solve the problem of how to achieve dynamic power control and resource allocation in a complex and changeable wireless network environment by integrating multi-dimensional data such as channel status, user needs, and network load to improve the overall energy efficiency and communication quality of the system. The technical solution is as follows:

[0006] In a first aspect, the present invention provides a wireless network communication method, comprising:

[0007] S100, integrated channel monitoring, data collection and preliminary processing functions, pre-processing of environmental perception and data;

[0008] S200, integrates adaptive power control and resource allocation functions to centrally handle cross-level optimization decisions;

[0009] According to the channel state evaluation results, the transmit power is adaptively adjusted, and the channel state evaluation function is used Calculate the current transmit power output;

[0010] According to user demand prediction and network load information, combined with high-order resource allocation partial differential equations: Intelligent allocation of network resources;

[0011] Unify cross-layer optimization of physical layer power control and network layer resource allocation;

[0012] S300, using edge computing capabilities to share the task of system architecture optimization and schedule computing and communication resources;

[0013] S400 uses virtualization technology to dynamically configure resources and centrally manage the overall architecture of the system.

[0014] Furthermore, the S100 includes steps S110-S130:

[0015] S110, real-time monitoring of the status of the wireless channel, including channel quality and spectrum utilization;

[0016] S120, continuously monitor network load status and record key performance indicators such as the current number of user connections, data traffic, and bandwidth utilization;

[0017] S130. Based on historical data and real-time monitoring information, predict the user's future needs, including traffic fluctuations and service quality requirements.

[0018] Furthermore, the S300 includes steps S310-S330:

[0019] S310, allocating computing tasks to edge nodes for execution;

[0020] S320, dynamically manage resource allocation of edge computing nodes, and optimize the utilization of edge resources according to real-time task requirements and network load conditions;

[0021] S330: Perform data localization processing at the edge node.

[0022] Furthermore, the S400 includes steps S410-S430:

[0023] S410, dynamically adjust the network architecture according to network demand and load changes;

[0024] S420, abstracting physical resources into a programmable logical resource pool through virtualization technology, and dynamically allocating resources;

[0025] S430, monitor the operating status of the system in real time, and when an abnormality is detected, automatically trigger the recovery mechanism to adjust resource allocation and isolate the faulty node.

[0026] Furthermore, the channel state evaluation function includes a combination of a time derivative of a signal-to-noise ratio, a channel attenuation coefficient, and a spectrum utilization rate, and the transmit power is dynamically calculated through the function.

[0027] Furthermore, the resource allocation decision is based on the following high-order resource allocation partial differential equation:

[0028] in, represents the amount of resources allocated to user u at time t; (t) is the demand forecast of user u at time t; (t) is the user priority factor; S(f,t) is the spectrum resource distribution; Δ, are the Laplace operator and the spatial gradient operator respectively; η, μ, ν are the optimization weight parameters.

[0029] Furthermore, the cross-level optimization decision is implemented through a cross-level power-resource collaborative model, which jointly optimizes the spatial distribution and time variation of power and resources.

[0030] Furthermore, the task distribution decision in S310 determines the optimal task distribution solution by optimizing the model calculation, combining the computing requirements of the task, the computing capabilities of the edge nodes and the network delay.

[0031] Furthermore, a wireless network communication system is provided, characterized in that it executes the wireless network communication method as described above.

[0032] In a second aspect, an embodiment of the present invention provides a wireless network communication system, including:

[0033] The intelligent perception and data processing module obtains channel status information in the wireless environment in real time through high-precision sensors and antenna arrays.

[0034] The collaborative optimization control module dynamically adjusts the transmit power through the channel state evaluation function.

[0035] The edge computing and resource scheduling module allocates computing tasks to edge nodes for execution, optimizing the utilization of edge resources.

[0036] Dynamic architecture management and virtualization module dynamically adjusts the network architecture and monitors the system operation status in real time.

[0037] As a preferred solution, an embodiment of the present invention provides a wireless network communication system for executing the above-mentioned method based on wireless network communication.

[0038] The key innovative features of the present invention include:

[0039] (1) Cross-layer collaborative optimization: The present invention achieves global optimization of system energy efficiency and communication quality by performing unified cross-layer optimization on power control of the physical layer and resource allocation of the network layer.

[0040] (2) Adaptive power control algorithm: Based on real-time channel status information and user demand prediction, the transmit power is dynamically adjusted to minimize energy consumption while ensuring communication quality.

[0041] (3) Intelligent resource allocation mechanism: Based on user demand prediction, network load and channel status information, network resources are intelligently allocated to prioritize the service quality of high-priority tasks and key users.

[0042] (4) Integration of edge computing and virtualization technology: Edge computing can be used to reduce the load on central servers, and physical resources can be abstracted into programmable logical resource pools through virtualization technology. Resources can be dynamically configured to respond to network changes, thereby improving system flexibility and resource utilization.

[0043] The following are its main beneficial effects:

[0044] The present invention proposes a wireless network communication method and system that comprehensively considers multi-dimensional data such as channel status, user demand and network load, and realizes efficient management of complex and changeable wireless network environments through dynamic power control and resource allocation strategies. Compared with traditional fixed power control and resource allocation methods, the present invention organically combines channel status evaluation, adaptive power adjustment, user demand prediction, and cross-level resource allocation through high-order mathematical formulas and optimization models, significantly improving the overall energy efficiency and communication quality of the system.

[0045] Specifically, the present invention can minimize energy consumption while ensuring communication quality by dynamically inputting channel status data such as signal-to-noise ratio, channel attenuation coefficient, and spectrum utilization into the power adjustment model; at the same time, by using the user demand prediction model and combining it with real-time network load information, it realizes intelligent allocation of network resources and gives priority to ensuring the service quality of high-priority tasks and key users. Through these innovative designs, the present invention not only improves the resource utilization of the wireless network system under high load and complex environments, but also enhances the system's ability to adapt to environmental changes, so that the system can ensure the stability of communication and the user's experience quality while maintaining efficient operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of a wireless network communication method provided by an embodiment of the present invention.

[0047] Figure 2 It is a structural block diagram of a wireless network communication method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention relates to a wireless network communication method and system, and belongs to the field of communication technology. The method includes: integrating channel monitoring, data acquisition and preliminary processing functions to realize real-time perception of the environment and data preprocessing; integrating adaptive power control and resource allocation functions to realize dynamic adjustment of transmission power and intelligent allocation of network resources through cross-level optimization decisions; utilizing edge computing capabilities to share system architecture optimization tasks, dynamically scheduling computing and communication resources; using virtualization technology to dynamically configure resources and centrally manage the overall architecture of the system. The invention significantly improves the energy efficiency and communication quality of the system through the comprehensive application of cross-level collaborative optimization, adaptive power control and edge computing technologies, and realizes efficient utilization of resources and flexible management of network architecture.

[0049] A wireless network communication method and system provided in an embodiment of the present invention are mainly used to solve the technical problem solved by the present invention, which is "how to realize dynamic power control and resource allocation in a complex and changeable wireless network environment by integrating multi-dimensional data such as channel status, user needs, network load, etc., so as to improve the overall energy efficiency and communication quality of the system."

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature. Example

[0052] Reference Figure 1 , is a flow chart of a wireless network communication method provided by an embodiment of the present invention, and the wireless network communication method may at least include steps S100-S400:

[0053] S100 integrates channel monitoring, data collection and preliminary processing functions, and is responsible for environmental perception and data preprocessing.

[0054] S200 integrates adaptive power control and resource allocation functions to centrally handle cross-level optimization decisions.

[0055] S300 uses edge computing capabilities to share the task of system architecture optimization and schedule computing and communication resources at the same time.

[0056] S400 uses virtualization technology to dynamically configure resources and centrally manage the overall architecture of the system.

[0057] S100, integrating channel monitoring, data collection and preliminary processing functions, responsible for environmental perception and data preprocessing. The step S100 at least includes steps S110-S130:

[0058] S110: Real-time monitoring of the status of the wireless channel, including channel quality and spectrum utilization. Specifically:

[0059] The system receives electromagnetic signals from the wireless environment through antenna arrays and RF front-end modules. The signals include communication data between user equipment and base stations and background noise. The system collects key data such as signal-to-noise ratio (SNR), channel attenuation coefficient, and spectrum utilization in real time. The data is extracted from the received original electromagnetic signal through digital signal processing technologies such as fast Fourier transform (FFT). Among them, the signal-to-noise ratio data directly reflects the quality of the communication link; the channel attenuation coefficient indicates the attenuation of the signal due to distance or obstacles during propagation; and the spectrum utilization data shows the usage of different frequency bands, including occupied frequency bands and idle frequency bands.

[0060] The system pre-processes the collected channel data to eliminate noise and possible interference information to ensure the accuracy of subsequent decisions. The system uses filters to denoise the signal-to-noise ratio data to eliminate the impact of environmental noise, thereby obtaining more accurate channel quality information. For channel attenuation data, the system uses dynamic modeling tools to evaluate and compensate for attenuation errors caused by changes in device position. At the same time, the spectrum utilization data is sorted to filter out outliers and short-term transient changes to ensure that the spectrum occupancy information obtained by the system is stable and continuous.

[0061] After completing data preprocessing, the system analyzes the channel status. The system identifies the stability of the current communication link and its possible interference sources by analyzing the changing trend of the signal-to-noise ratio. By combining the channel attenuation coefficient and spectrum utilization, the system dynamically evaluates the availability of the current channel and provides a real-time reference for subsequent resource allocation and power control. When the signal-to-noise ratio is lower than a predetermined threshold, the system may recommend adjusting the transmit power or switching the frequency band. Finally, the system uses the above analysis results as input data and passes them to the collaborative optimization control module for optimization in subsequent power control and resource allocation decisions.

[0062] S120, continuously monitor the network load status and record key performance indicators such as the current number of user connections, data traffic, and bandwidth utilization. Specifically:

[0063] The system collects load data of each communication node in real time through network monitoring tools. The load data includes key performance indicators such as the current number of user connections, data flow, and bandwidth utilization. For example, the number of user connections reflects the number of currently active users, the data flow shows the amount of communication data exchanged between users, and the bandwidth utilization indicates the use of available network resources. The data is reported to the system in real time by network devices (such as routers, switches, and base stations) through network interface protocols (such as SNMP, NetFlow) to ensure the accuracy and timeliness of the data.

[0064] The system preprocesses the collected network load data to remove noise and outliers, and performs necessary formatting to ensure the consistency and availability of the data. For example, when multiple users are connected to the network at the same time, instantaneous traffic peaks may occur. The system smoothes the data using a sliding average or exponential smoothing algorithm to obtain a more stable load indicator. The preprocessed data is also compressed and standardized so that the system can quickly analyze and process it in subsequent steps, while ensuring the system's sensitivity to sudden load changes.

[0065] After completing the data preprocessing, the system conducts an in-depth analysis of the network load to identify the current network congestion points and potential bottlenecks. The system identifies network areas that may be overloaded or resource-deficient by analyzing the changing trend of bandwidth utilization. The system also tracks the fluctuations in the number of user connections and data traffic, determines whether the current load level is close to the critical point of the system, and predicts future load trends. The analysis results will be passed as input data to the collaborative optimization control module so that the module can make optimization decisions based on accurate network load information in subsequent resource allocation and power control. For example, when bandwidth utilization is close to saturation, the system may adjust the resource allocation strategy to prioritize the quality of service for high-priority users.

[0066] S130: Based on historical data and real-time monitoring information, predict the user's future needs, including traffic fluctuations and service quality requirements. Specifically:

[0067] The system collects data related to user behavior in real time through network monitoring tools and user devices. The data includes key indicators such as user connection duration, data usage, service type preference, and usage frequency. For example, the system records the connection duration of each user to determine the peak usage time period, the data usage reflects the user's bandwidth demand, and the service type preference (such as video streaming, web browsing, online games, etc.) shows the user's demand intensity for specific resources. After preliminary processing, the collected data is stored in the system's database for subsequent historical trend analysis and real-time monitoring.

[0068] The system uses data mining and machine learning techniques to conduct in-depth analysis of stored user behavior data to identify historical patterns of user demand. For example, the system uses time series analysis to identify regular changes in user demand for resources during specific time periods (such as daily peak hours). The system uses cluster analysis to group users with similar demand patterns and predict future resource demand based on the grouped data. The pattern recognition can not only predict overall demand trends, but also refine demand changes for specific user groups.

[0069] On the basis of completing the historical data analysis, the system combines the user behavior data monitored in real time to predict future user needs. For example, by real-time monitoring of the current user connection status and data usage, the system can dynamically adjust the parameters of the prediction model to improve the accuracy of the prediction. The system uses prediction algorithms, such as ARIMA models or LSTM models based on deep learning, to predict future traffic fluctuations and service quality requirements. The prediction model predicts the bandwidth requirements, service quality requirements and possible traffic peaks for the next time period by analyzing the input real-time data and historical patterns. The prediction results are passed to the collaborative optimization control module as input data to support its early adjustment of resource allocation strategies and power control parameters, thereby ensuring that the system can respond to the expected needs of users.

[0070] S200, integrating adaptive power control and resource allocation functions, centrally processing cross-level optimization decisions. Step S200 at least includes steps S210-S230:

[0071] S210: Adaptively adjust the transmit power according to the channel state information. Specifically:

[0072] The system receives the user demand prediction result calculated in step S130 As well as real-time monitoring of channel status information, including key indicators such as signal-to-noise ratio SNR(t), channel attenuation coefficient A(t) and spectrum utilization U(f,t).

[0073] Based on the channel state information, the system calculates through the following channel state evaluation function C(t):

[0074] Wherein, U(f,t) represents the spectrum utilization rate of frequency f at time t, which measures the usage ratio of the frequency. The evaluation function C(t) reflects the comprehensive state of the current channel and serves as the basis for subsequent transmission power adjustment. represents the rate of change of the signal-to-noise ratio over time, , , is the weight coefficient of each parameter, which is used to control the influence of each factor on power adjustment.

[0075] C(t): Channel state evaluation function, which represents the comprehensive evaluation value of the channel environment at time t.

[0076] SNR(t): Signal-to-noise ratio, which represents the signal-to-noise ratio of the wireless channel at time t.

[0077] SNR(t): The time rate of change of signal-to-noise ratio, reflecting the dynamic changes of signal quality.

[0078] A(t): Channel attenuation coefficient, which represents the propagation attenuation intensity of the signal at time t.

[0079] U(f,t): spectrum utilization, which indicates the resource occupancy ratio of frequency f at time t.

[0080] Based on the output of the channel state evaluation function C(t), the system uses the adaptive power adjustment formula Calculate the transmit power at the current time t:

[0081]

[0082] When the C(t) value is high, it means the channel condition is good and the system will reduce the transmit power. ; When the C(t) value is low, the system will increase the transmit power to ensure communication quality.

[0083] : Transmitting power output at time t. Maximum transmit power, the power limit of the system.

[0084] C(t) channel state estimation value, comes from the channel state estimation function. It is the reference value of the channel state, which refers to the reference channel state value when the system can communicate normally. β adjustment coefficient controls the sensitivity of power adjustment.

[0085] The system optimizes the objective function through the following energy efficiency Optimize global energy efficiency:

[0086]

[0087] in, It is the target value for energy efficiency optimization, representing the minimum combined loss of power consumption and signal quality deviation during the entire optimization cycle. is the transmit power output, which comes from the power adjustment model. λ is the weight factor, which balances the power consumption and the degree of signal quality loss. Q(t) is the real-time network load function, which indicates the network load level. SNR(t) is the signal-to-noise ratio, which indicates the quality of the communication link. The minimum acceptable signal-to-noise ratio is the minimum requirement to ensure communication quality.

[0088] The system dynamically adjusts the transmission power , and combined with the real-time calculated network load Q(t) to optimize the system's energy efficiency performance.

[0089] After completing the above power adjustment calculation, the system will adjust the transmission power The control command is transmitted to the relevant communication equipment or base station to actually adjust the transmission power. The system continuously monitors the communication effect after adjustment through the feedback loop, and makes real-time adjustments and optimizations according to the dynamic changes of the channel status to ensure the balance between energy consumption and communication quality.

[0090] S220: Dynamically allocate network resources, combine user demand forecasts, and prioritize resources for high-priority tasks and key users. Specifically:

[0091] The system combines the user demand prediction results from step S130 and the channel state evaluation result C(t) in step S210 provide input data for resource allocation calculation. The system uses the user demand prediction model and channel status evaluation result C(t) to determine the priority and allocation strategy of user resource requirements.

[0092] The system is implemented by the following high-order resource allocation partial differential equation Calculate the resource allocation for each user at time t:

[0093]

[0094]

[0095] in, represents the amount of resources allocated to user u at time t; (t) is the demand forecast of user u at time t; (t) is the user priority factor; S(f,t) is the spectrum resource distribution; Δ, are the Laplace operator and the spatial gradient operator respectively; η, μ, ν are the optimization weight parameters.

[0096] The system combines the user demand prediction, priority factor and spectrum utilization through the equation to optimize the resource allocation strategy and ensure that the resources are reasonably and efficiently allocated to each user.

[0097] The system allocates resources based on the calculated The allocation instructions are delivered to each network node to ensure that high-priority tasks and key users get the required resource support. The system monitors the execution effect of resource allocation in real time and dynamically adjusts the resource allocation strategy based on feedback information to ensure the optimal use of network resources.

[0098] S230: Perform unified cross-layer optimization on physical layer power control and network layer resource allocation. Specifically:

[0099] The system receives and integrates the power adjustment and resource allocation results from steps S210 and S220 to construct a cross-level collaborative optimization model.

[0100] The system will transmit power at the physical layer and network layer resource allocation Fusion to cross-level power-resource coordination model In the above example, the goal of global energy efficiency optimization is calculated.

[0101] The system is optimized by the following global energy efficiency equation To optimize:

[0102]

[0103] in, Optimize the objective function for global energy efficiency. is a cross-level collaborative optimization function, which represents the synergistic effect of transmit power and resource allocation. is the transmit power function. is the resource allocation function. γ is the optimization weight coefficient, which adjusts the balance between signal quality and resource consumption. is the signal-to-noise ratio of user u at time t. is the minimum acceptable signal-to-noise ratio. Ω is the system coverage, indicating the optimized spatial area.

[0104] The system calculates optimization equations and dynamically adjusts power control and resource allocation strategies to achieve coordinated optimization of the physical layer and network layer, ensuring that global energy efficiency reaches the optimal state.

[0105] After completing the optimization calculation, the system updates the transmission power through the iterative optimization formula according to the global energy efficiency optimization result. and resource allocation The optimal value of:

[0106]

[0107] The system gradually converges to the optimal solution through real-time feedback and iterative adjustment, and applies the final optimization results to actual operations to execute specific power control and resource allocation decisions.

[0108] S300: Utilize edge computing capabilities to share the task of optimizing the system architecture and schedule computing and communication resources. Step S300 at least includes steps S310-S330:

[0109] S310: Allocate computing tasks to edge nodes for execution. Specifically:

[0110] The system receives input data from the intelligent perception and data processing module, including the computing requirements of the task, network status (such as bandwidth utilization B(t) and network latency L(t)), and the computing power of the edge node. The system combines the user demand prediction results calculated in step S130 ,Analyze the computing requirements and network status of the task, and match the tasks suitable for execution on the edge nodes.,The analysis process comprehensively considers the real-time requirements of the task and the computing power of the edge nodes, ensuring the best match between the task and the node.

[0111] After task analysis and matching are completed, the system uses an optimization algorithm to make task distribution decisions, as follows:

[0112]

[0113] Among them, the is the set of tasks assigned to edge node e at time t, The computational requirements of the task, is the computing power of the edge node, is the network delay of the edge node, and λ are weight parameters.

[0114] The decision-making process is based on the formula, comprehensively considers the task computing requirements and the computing resources and network status of the edge nodes, and achieves optimal task distribution by minimizing computing resource occupancy and network latency.

[0115] After the task distribution decision is completed, the system will pass the task allocation instruction to the corresponding edge node to actually perform the task calculation processing. The system monitors the execution progress and results of the task in real time and dynamically adjusts the task distribution strategy through a feedback mechanism. For example, when the load of an edge node increases or the network delay increases, the system will reallocate some tasks to other nodes to avoid overload and ensure that the response speed of task processing meets expectations.

[0116] S320, dynamically manage the resource allocation of edge computing nodes, and optimize the utilization of edge resources according to real-time task requirements and network load conditions. Specifically:

[0117] The system collects real-time resource status information from edge nodes, including computing power , memory usage , Network bandwidth utilization , and the length of the task queue And other key indicators.

[0118] Combined with the task distribution decision result in step S310 ,The system analyzes the load of each edge node and determines whether the current resource usage ,status needs to be optimized and adjusted.

[0119] After the resource status analysis is completed, the system makes resource allocation decisions using the following optimization formula:

[0120]

[0121] Among them, the is the amount of resources allocated to edge node e at time t, The computational requirements of the task, is the computing power of the edge node, is the task queue length, is the memory usage, and is the weight parameter.

[0122] The system dynamically adjusts resource allocation strategies according to real-time task requirements and resource status of edge nodes to ensure optimal utilization of edge resources.

[0123] After making resource allocation decisions, the system sends resource allocation instructions to each edge node to perform specific resource adjustment operations. The system dynamically monitors the resource usage of each edge node and adjusts the resource allocation strategy in real time. For example, when the task queue length of an edge node is When the threshold is exceeded, the system will transfer some tasks to other nodes with lower loads to ensure the effective use of edge resources and avoid resource overload.

[0124] S330: Perform data localization processing at the edge node. Specifically:

[0125] The system receives real-time data from the intelligent sensing and data processing module, including sensor data , User Generated Content , network traffic data Etc. Combined with the resource allocation result in step S320 ,The system analyzes data processing requirements and determines which data is suitable for local processing at the edge nodes to reduce the need for data backhaul centers.

[0126] The system is based on the computing power of edge nodes , Storage Space and network status (such as bandwidth utilization and network latency ), the data localization processing decision is made through the following optimization formula:

[0127]

[0128] Among them, the is the data set processed at edge node e at time t, is the data size, is the storage space of the edge node, is the computational complexity of the data, For computing power, For network delay, and is the weight parameter.

[0129] After determining the data processing strategy, the system assigns the processing tasks to the corresponding edge nodes for localized data processing. The system monitors the processing progress and results in real time and dynamically adjusts the processing strategy through a feedback mechanism. When an edge node is overloaded or the network delay increases, the system will transfer part of the data processing tasks to other nodes to ensure that the overall data processing efficiency and network delay are controlled within the predetermined range.

[0130] S400, using virtualization technology to dynamically configure resources and centrally manage the overall architecture of the system. Step S400 at least includes steps S410-S430:

[0131] S410: Dynamically adjust the network architecture according to network demand and load changes. Specifically:

[0132] The system collects real-time data of network status through intelligent perception and data processing modules, including bandwidth utilization B(t), network delay L(t), node load , and the number of user connections The system is based on the user demand forecast data in step S130. , analyze the current network status and determine whether the network architecture needs to be adjusted to cope with current or predicted load changes. For example, when the bandwidth utilization B(t) is close to saturation, the system will consider adjusting the virtual network function configuration or re-dividing the physical resources.

[0133] After analyzing the network status data, the system uses virtualization technology to reconfigure virtual network functions (VNFs) according to current needs, including enabling, disabling or migrating virtual network functions to ensure efficient use of network resources.

[0134] The system reconfigures virtual network functions through the following optimization model:

[0135]

[0136] Among them, the It is represented by the amount of resources allocated to the virtual network function v at time t, is the node load of virtual network function v, L(v,t) is the network delay, and is the weight parameter.

[0137] The configuration decision model comprehensively considers the computing resource occupancy and network latency of the virtual network function, dynamically adjusts the deployment location and resource allocation of the virtual network function, and ensures that the system can flexibly adjust the architecture under load changes.

[0138] The system dynamically adjusts the logical division of physical resources according to changes in network load, including reconfiguring physical servers, storage resources and network devices to support the needs of different virtual network functions.

[0139] The system performs logical division of physical resources through the following optimization formula:

[0140]

[0141] Among them, the represents the amount of resources that the physical resource r is divided into at time t, is the amount of resources allocated to the virtual network function v, is the current available capacity of the physical resource r, It is the utilization rate of physical resources.

[0142] Through this logical partitioning model, the system flexibly adjusts the allocation of physical resources to ensure that each virtual network function obtains the required computing, storage and network resources, thereby optimizing the overall network architecture performance and resource utilization efficiency.

[0143] After the system completes the adjustment decision of the network architecture, it will issue configuration and division instructions to the relevant physical devices and virtual network function modules to perform the actual configuration adjustment. The system will monitor the effect of the network architecture adjustment in real time and dynamically adjust the architecture configuration based on the feedback information. For example, when the load of a certain node suddenly increases or the network delay increases significantly, the system will immediately adjust the deployment of virtual network functions or re-divide physical resources to alleviate the load pressure.

[0144] S420: Through virtualization technology, physical resources are abstracted into programmable logical resource pools, and resources are dynamically allocated. Specifically:

[0145] The system uses virtualization technology to abstract physical servers, storage devices and network resources into logical resource pools, which include computing resource pools (such as CPU and memory), storage resource pools, and network resource pools (such as bandwidth and connection ports). The establishment of the resource pool is based on the real-time data received by the system from the intelligent perception and data processing module, such as CPU utilization. , memory utilization , Storage occupancy and network bandwidth utilization .

[0146] The system predicts data based on the user demand in step S130 , combined with real-time network status data, such as the number of user connections , Bandwidth requirements , Service Quality Requirements , and network latency , determine the amount of logical resources required for each service request. The system analyzes the requirements and the current resource pool status and formulates a resource allocation plan to ensure that user requirements are met under different network conditions.

[0147] The system uses an optimization algorithm to dynamically allocate resources through the following formula:

[0148]

[0149] Among them, the represents the amount of logical resources allocated at time t, The bandwidth demand requested by the user, is the network bandwidth utilization, is the number of user connections, is the CPU utilization, is the storage utilization, is the network delay, and λ is the weight parameter.

[0150] The system ensures optimal allocation of resources by comprehensively considering the real-time status and demand of various resources, while taking into account resource utilization and service quality.

[0151] After executing resource allocation, the system continuously monitors the usage of various resources, especially during high-load periods or sudden demands, and adjusts resource allocation in real time. When it detects that a resource pool is close to saturation or network latency increases, the system automatically triggers reallocation, transferring some resources from low-priority tasks to high-priority tasks to ensure that service quality is not affected.

[0152] S430: Monitor the operating status of the system in real time, and automatically trigger the recovery mechanism to adjust resource allocation and isolate faulty nodes when an abnormality is detected.

[0153] The system collects health status indicators in real time through intelligent perception and data processing modules, and the indicators include node CPU utilization , memory utilization , network delay , service response time And system error log The system continuously evaluates the system health status by monitoring the indicators in real time, and determines whether there are potential failures based on the set baselines and thresholds.

[0154] The system uses anomaly detection algorithms to analyze the collected data and initiates the fault identification process when an anomaly is detected. For example, when the CPU utilization Continuously high or network delay If the number of faults increases significantly, the system determines it as a potential fault and conducts further analysis to determine the faulty node or service.

[0155] Furthermore, after confirming the fault, the system will automatically trigger the recovery mechanism, including the following operations: Resource reallocation: The system reallocates computing, storage and network resources according to the current resource status and demand to reduce the burden on the faulty node. Faulty node isolation: The system dynamically adjusts the network architecture to isolate the faulty node to prevent it from affecting the operation of other normal nodes. Service migration and restart: If a service is abnormal, the system will migrate it to other nodes or restart the service to restore normal operation.

[0156] The system continuously monitors the recovery effect, verifies whether key indicators have returned to normal, and dynamically adjusts the recovery strategy based on feedback information. The system also optimizes the recovery mechanism and improves the system's self-healing ability based on long-term data analysis. Example

[0157] Figure 2 1 is a block diagram of a wireless network communication method and system provided by an embodiment of the present invention. Figure 2As shown, the system may include:

[0158] The intelligent sensing and data processing module 10 is used to integrate channel monitoring, data collection and preliminary processing functions. The module uses high-precision sensors and antenna arrays to obtain channel status information in the wireless environment in real time, including key parameters such as signal-to-noise ratio (SNR), channel attenuation coefficient and spectrum utilization. The system denoises, preprocesses and standardizes the collected data to ensure the accuracy and consistency of the data. Specifically:

[0159] The intelligent perception and data processing module 10 first uses high-precision sensors and antenna arrays to collect channel state information in the wireless environment in real time. Specifically, the channel state information acquired by module 10 includes key parameters such as signal-to-noise ratio (SNR(t)), channel attenuation coefficient (A(t)), and spectrum utilization (U(f,t)). The SNR(t) refers to the signal-to-noise ratio of the received signal at time t, which directly reflects the quality of the communication link. The A(t) represents the attenuation of the signal due to distance or obstacles during propagation. The U(f,t) is used to indicate the utilization rate of frequency f at time t.

[0160] The module 10 performs denoising on the collected channel data and uses a filter to remove environmental noise to obtain more accurate channel quality information. Furthermore, the module also preprocesses and standardizes the data to ensure the consistency and accuracy of the data. The preprocessed channel state information will be passed as input data to subsequent modules for processing.

[0161] The collaborative optimization control module 20 is responsible for integrating adaptive power control and resource allocation functions, and centrally processing cross-level optimization decisions. This module dynamically adjusts the transmit power through the channel state evaluation function to ensure that energy consumption is reduced while meeting the communication quality. At the same time, this module intelligently allocates network resources based on user demand prediction and network load information to achieve unified optimization of physical layer power control and network layer resource allocation. Specifically:

[0162] Based on the channel state information transmitted by the module 10, the collaborative optimization control module 20 is responsible for making cross-level optimization decisions for adaptive power control and resource allocation. Specifically, the module 20 operates through the following steps:

[0163] Channel state evaluation and power adjustment: The module 20 performs channel state evaluation through a channel state evaluation function C(t), where:

[0164] in, represents the rate of change of the signal-to-noise ratio over time, , , is the weight coefficient of each parameter, which is used to control the influence of each factor on power adjustment. Based on the result of the channel state evaluation function, module 20 dynamically adjusts the transmission power , the specific formula is:

[0165] in, is the maximum transmission power, β is the adjustment parameter, It is the reference value of the channel status.

[0166] Resource allocation and cross-layer optimization: The module 20 intelligently allocates network resources based on user demand prediction information and network load information. By combining the power control of the physical layer and the resource allocation of the network layer, the module achieves unified cross-layer optimization, so that the system can achieve global optimization at different levels.

[0167] The edge computing and resource scheduling module 30 uses edge computing capabilities to share the optimization tasks of the system architecture and dynamically schedule computing and communication resources. This module is responsible for allocating appropriate computing tasks to edge nodes for execution, reducing the load on the central server and improving the response speed of task processing. In addition, the module optimizes the utilization of edge resources based on real-time task requirements and network load conditions, and performs data localization processing at edge nodes to reduce the demand for data return and network latency. Specifically:

[0168] The edge computing and resource scheduling module 30 uses edge computing capabilities to share the optimization tasks of the system architecture and dynamically schedule computing and communication resources. Specifically, the module 30 operates through the following steps:

[0169] Task allocation and edge computing: The module 30 allocates appropriate computing tasks to edge nodes for execution, reducing the load on the central server. The computing tasks include processing tasks with high computing requirements, which are processed by edge nodes through their computing power. Distributed processing is performed to improve response speed.

[0170] Edge resource management and data localization processing: Module 30 further optimizes the utilization of edge resources based on real-time task requirements and network load conditions. Edge nodes process local data, reducing the need for data backhaul to central servers, thereby reducing network latency.

[0171] The dynamic architecture management and virtualization module 40 uses virtualization technology to dynamically configure resources and centrally manage the overall architecture of the system. The module dynamically adjusts the network architecture according to network demand and load changes, including reconfiguration of virtual network functions and logical division of physical resources, to ensure the flexibility and efficiency of the system. By abstracting physical resources into logical resource pools, the module can flexibly and efficiently allocate resources to cope with different network conditions and user needs. At the same time, the module has the function of real-time monitoring of the system operation status. When an anomaly is detected, the recovery mechanism is automatically triggered to adjust resource allocation and isolate the faulty node to ensure the stability and high availability of the system. Specifically:

[0172] Creation and allocation of virtualized resource pools: The module 40 first abstracts physical resources into logical resource pools, including computing resource pools, storage resource pools, and network resource pools. According to real-time network requirements and user requests, the module 40 dynamically allocates logical resources to cope with different network conditions and requirements.

[0173] System health monitoring and automatic recovery: The module 40 monitors the system operation status in real time and collects key indicators including node CPU utilization, memory utilization, network latency and system error logs. When an abnormality is detected, the module 40 automatically triggers the recovery mechanism, adjusts resource allocation and isolates the faulty node to ensure stable operation of the system.

[0174] The key innovative features of the present invention include:

[0175] (1) Cross-layer collaborative optimization: The present invention achieves global optimization of system energy efficiency and communication quality by performing unified cross-layer optimization of physical layer power control and network layer resource allocation.

[0176] (2) Adaptive power control algorithm: Based on real-time channel status information and user demand prediction, the transmit power is dynamically adjusted to minimize energy consumption while ensuring communication quality.

[0177] (3) Intelligent resource allocation mechanism: Based on user demand prediction, network load and channel status information, network resources are intelligently allocated to prioritize the service quality of high-priority tasks and key users.

[0178] (4) Integration of edge computing and virtualization technology: Edge computing can be used to reduce the load on central servers, and physical resources can be abstracted into programmable logical resource pools through virtualization technology. Resources can be dynamically configured to respond to network changes, thereby improving system flexibility and resource utilization.

[0179] The following are its main beneficial effects:

[0180] The present invention proposes a wireless network communication method and system that comprehensively considers multi-dimensional data such as channel status, user demand and network load, and realizes efficient management of complex and changeable wireless network environments through dynamic power control and resource allocation strategies. Compared with traditional fixed power control and resource allocation methods, the present invention organically combines channel status evaluation, adaptive power adjustment, user demand prediction, and cross-level resource allocation through high-order mathematical formulas and optimization models, significantly improving the overall energy efficiency and communication quality of the system.

[0181] Specifically, the present invention can minimize energy consumption while ensuring communication quality by dynamically inputting channel status data such as signal-to-noise ratio, channel attenuation coefficient, and spectrum utilization into the power adjustment model; at the same time, by using the user demand prediction model and combining it with real-time network load information, it realizes intelligent allocation of network resources and gives priority to ensuring the service quality of high-priority tasks and key users. Through these innovative designs, the present invention not only improves the resource utilization of the wireless network system under high load and complex environments, but also enhances the system's ability to adapt to environmental changes, so that the system can ensure the stability of communication and the user's experience quality while maintaining efficient operation.

[0182] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, and the above should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A wireless network communication method, characterized in that: The method comprises: S100, integrated channel monitoring, data collection and preliminary processing functions, pre-processing of environmental perception and data; S200, integrates adaptive power control and resource allocation functions to centrally handle cross-level optimization decisions; According to the channel state evaluation results, the transmit power is adaptively adjusted, and the channel state evaluation function is used Calculate the current transmit power output; Among them, C(t) reflects the comprehensive state of the current channel and serves as the basis for subsequent transmit power adjustment. A(t) is the channel attenuation coefficient. U(f, t) represents the spectrum utilization of frequency f at time t, which measures the usage ratio of the frequency. represents the rate of change of the signal-to-noise ratio over time, is the weight coefficient of each parameter, which is used to control the influence of each factor on power adjustment; Based on the predicted user demand and network load information, combined with high-order resource allocation partial differential equations Intelligent allocation of network resources: in, represents the amount of resources allocated to user u at time t; It represents the second-order derivative of resource allocation with respect to time t, describing the dynamic trend of resource allocation over time; (t) is the demand forecast of user u at time t; (t) is the user priority factor; It represents the second-order derivative of user priority over time, describing the dynamic change rate of priority; S(f,t) is the spectrum resource distribution; The second-order Laplace operator represents spectrum allocation and describes the distribution gradient of spectrum resources in space; Δ are the Laplace operator and the spatial gradient operator respectively; η, μ, ν are the optimization weight parameters; Unify cross-layer optimization of physical layer power control and network layer resource allocation; S300, using edge computing capabilities to share the task of system architecture optimization and schedule computing and communication resources; S400 uses virtualization technology to dynamically configure resources and centrally manage the overall architecture of the system.

2. The method according to claim 1, characterized in that The S100 includes steps S110-S130: S110, real-time monitoring of the status of the wireless channel, including channel quality and spectrum utilization; S120, continuously monitor the network load status and record the current number of user connections, data traffic, and key performance indicators of bandwidth utilization; S130. Based on historical data and real-time monitoring information, predict the user's future needs, including traffic fluctuations and service quality requirements.

3. The method according to claim 1, characterized in that The S300 includes steps S310-S330: S310, allocating computing tasks to edge nodes for execution; S320, dynamically manage resource allocation of edge computing nodes, and optimize the utilization of edge resources according to real-time task requirements and network load conditions; S330: Perform data localization processing at the edge node.

4. The method according to claim 1, characterized in that: The S400 includes steps S410-S430: S410, dynamically adjust the network architecture according to network demand and load changes; S420, abstracting physical resources into a programmable logical resource pool through virtualization technology, and dynamically allocating resources; S430, monitor the operating status of the system in real time, and when an abnormality is detected, automatically trigger the recovery mechanism to adjust resource allocation and isolate the faulty node.

5. The method according to claim 1, characterized in that The cross-level optimization decision is implemented through a cross-level power-resource collaborative model, which jointly optimizes the spatial distribution and time variation of power and resources.

6. The method according to claim 3, characterized in that The task distribution decision in S310 determines the optimal task distribution solution by optimizing model calculation, combining the computing requirements of the task, the computing capabilities of the edge nodes and the network delay.

7. A wireless network communication system, used to execute the wireless network communication method according to any one of claims 1 to 6, characterized in that: The system comprises: Intelligent sensing and data processing module, which obtains channel status information in the wireless environment in real time through high-precision sensors and antenna arrays; The collaborative optimization control module dynamically adjusts the transmit power through the channel state evaluation function; The edge computing and resource scheduling module allocates computing tasks to edge nodes for execution, optimizing the utilization of edge resources; Dynamic architecture management and virtualization module dynamically adjusts the network architecture and monitors the system operation status in real time.

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