5G Communication Network Base Station Node Management System Based on Cloud Computing

Abstract

The present invention discloses a 5G communication network base station node management system based on cloud computing, which relates to the technical field of communication networks and includes a distributed base station node cluster, a cloud resource computing unit, an edge computing unit, and a resource scheduling unit; by setting up a distributed base station node cluster, the present invention densely deploys 5G communication network base stations according to the demand assessment based on the pedestrian flow data and vehicle flow data in the distribution area to improve the total network capacity, obtains the operation data of all 5G communication network base stations within the regional scope, performs three-dimensional pooling management on the computing, storage, and spectrum resources of the base stations, and simultaneously constructs a traffic prediction model based on historical data to predict the resource requirements of real-time traffic. Based on the resource requirement values, computing, storage, and network resources are dynamically allocated to each base station node to obtain each communication resource link, the communication data of the corresponding base station nodes are obtained, and link transmission evaluation is performed based on the communication data to ensure the security and stability during the transmission process.

Description

Technical Field

[0001] The present invention relates to the technical field of communication networks, and particularly to a 5G communication network base station node management system based on cloud computing. Background Art

[0002] The base station nodes of the 5G communication network are key components in the 5G wireless communication system, responsible for providing wireless access services, forwarding data, and supporting communication between user equipment (such as mobile phones, Internet of Things devices, etc.) and the network. In the 5G network, the functions and architectures of the base station nodes have changed significantly compared to 4G to support higher speeds, lower latency, and larger capacities;

[0003] The base station nodes provide wireless signal coverage and access services to ensure that user equipment can communicate with the network. In 5G, the base station provides higher transmission rates and lower latency through new technologies; at the same time, with the gradual maturity of vehicle networking technology and the advent of the big data era, more and more vehicles have intelligent services such as autonomous driving and environmental perception. These services have a sharp increase in communication resource requirements, resulting in a decline in the utilization efficiency of the spectrum resources of the vehicle networking. The existing network base station management requirements do not take into account the traffic usage requirements of vehicles, which is likely to cause network latency in the actual application process;

[0004] In view of the above technical deficiencies, a solution is proposed. Summary of the Invention

[0005] The purpose of the present invention is to: by setting up a distributed base station node cluster, conduct demand assessment based on the pedestrian flow data and vehicle flow data in the distribution area, densely deploy 5G communication network base stations to improve the total capacity of the network, and obtain the operation data of all 5G communication network base stations within the regional scope, perform three-dimensional pooling management on the computing, storage, and spectrum resources of the base stations, and conduct link transmission assessment based on communication data, which can not only meet the traffic demand but also ensure the security and stability during the transmission process.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: a 5G communication network base station node management system based on cloud computing, including a distributed base station node cluster, a cloud resource computing unit, an edge computing unit, and a resource scheduling unit;

[0007] The distributed base station node cluster is composed of multiple radio access units supporting the 5G NR standard, conducts demand assessment based on the pedestrian flow data and vehicle flow data in the distribution area, and densely deploys 5G communication network base stations according to the demand assessment results to improve the total capacity of the network;

[0008] The cloud resource computing unit includes a resource management module and a demand acquisition module. The resource management module is used to obtain the operation data of all 5G communication network base stations within the regional scope, and perform three-dimensional pooling management of the computing, storage, and spectrum resources of the base stations;

[0009] The demand acquisition module is used to obtain the real-time vehicle flow data and real-time pedestrian flow data within the regional scope, and at the same time obtain the peak factor within the region. Based on historical data, a traffic prediction model is constructed to predict the resource demand for real-time traffic, and the resource demand value is sent to the resource scheduling unit;

[0010] The resource scheduling unit is used to obtain and process the resource demand value, and dynamically allocate computing, storage, and network resources to each base station node based on the resource demand value to obtain each communication resource link;

[0011] The edge computing node is used to obtain each communication resource link, and obtain the communication data of the corresponding base station nodes. Based on the communication data, link transmission evaluation is performed, local traffic shaping and fault pre-judgment are executed according to the evaluation results, and a warning instruction is generated according to the fault pre-judgment result.

[0012] Furthermore, the specific process of densely deploying 5G communication network base stations according to the demand evaluation result is as follows:

[0013] S101. Obtain the distribution area of the 5G communication network base station and calculate the area of the region;

[0014] S102. Obtain the normal pedestrian flow and normal vehicle flow in the distribution area. The pedestrian flow data specifically includes the maximum pedestrian flow, normal pedestrian flow, and their corresponding time periods. The vehicle flow data specifically includes the maximum vehicle flow, normal vehicle flow, and their corresponding time periods;

[0015] S103. Calculate the maximum traffic demand according to the maximum pedestrian flow data and maximum vehicle flow data, set the large traffic demand area according to their corresponding time periods, set the number of 5G communication network base stations according to the maximum traffic demand, and perform uniform density distribution setting according to the area of the region to obtain the coordinates of each traffic base station node;

[0016] S104. Calculate the normal traffic demand according to the normal pedestrian flow data and normal vehicle flow data, set the normal traffic demand area according to their corresponding time periods, and select normal base station nodes and peak standby base station nodes according to the regional distribution situation among the coordinates of each traffic base station node.

[0017] Furthermore, the base station node is also provided with a breathing control module, which is used for the adaptive adjustment mechanism according to the state switching threshold, and at the same time follows the power coordination control protocol with adjacent base stations.

[0018] Furthermore, the specific process of obtaining the resource demand value is as follows:

[0019] S201. Obtain historical data, where the historical data includes historical traffic flow data, historical pedestrian flow data, and corresponding time nodes. Calculate the real-time traffic demand according to the base station traffic demand calculation formula, and integrate the real-time traffic demand and time nodes as training samples. Divide the generated training samples into a training set and a test set according to a ratio of 8:2.

[0020] The specific base station traffic demand calculation formula is: Base station traffic demand = (real-time traffic flow data + real-time pedestrian flow data) × average traffic per user × peak factor.

[0021] S202. Download the weight file and load it onto the corresponding network to initialize the transfer network parameters, and determine the number of hidden layer nodes of the BP neural network model according to the number of samples in the training set.

[0022] S203. Modify the last fully connected layer of the network, keep the input unchanged, set the output to the resource demand value, initialize the weights of the last layer, use the gradient descent algorithm for learning, and use fixed-step decay to optimize the training parameters. Retrain the entire network to obtain a traffic prediction model.

[0023] S204. During the training process, randomly and without repetition extract small batches of training samples from the training set. After extracting all the training samples in the training set, it is one training cycle. Iterate to a certain number of cycles to complete the training, and then use the test set to evaluate the effect of the traffic prediction model.

[0024] S205. Substitute the real-time traffic flow data and real-time pedestrian flow data into the traffic prediction model to obtain the resource demand value.

[0025] Further, the specific process of obtaining each communication resource link is as follows:

[0026] S301. Obtain the status information of each communication base node, and generate a base station status prediction matrix through the digital twin engine.

[0027] S302. Use the federated learning engine to aggregate multi-base station local models to generate a global policy.

[0028] S303. Solve the constrained optimization problem through the resource scheduling unit:

[0029] , where U j is the user utility function, N is the number of users, that is, the sum of the real-time traffic flow data and real-time pedestrian flow data, r j is the preset allocated resource amount, and R total is the maximum traffic limit value of the base station.

[0030] Furthermore, the specific process of evaluating link transmission based on communication data is as follows:

[0031] S401. Collect the communication data of the base station nodes. The communication data includes signal strength Ei, traffic value Qi, and signal-to-noise ratio dBi. Calculate the interference impact index Ui according to the communication data: , where i = 1, 2, 3, …, n, and n is the number of communication data. Among them, is the standard signal strength, is the standard traffic value, is the standard signal-to-noise ratio, and α, β, and γ are preset proportionality coefficients. Evaluate the communication quality of the link through the interference impact index. The larger the interference impact index, the worse the communication quality of the link. On the contrary, the smaller the interference impact index, the better the communication quality of the link;

[0032] S402. Collect the link operation stability information. The link operation stability information includes transmit signal power Wi, packet loss rate Hi, and throughput Di;

[0033] S403. Obtain the interference impact index and the link operation stability information. Calculate the communication link operation fluctuation coefficient Yi according to the following formula: , where e1, e2, and e3 are preset weight coefficients. The communication link operation fluctuation coefficient is used to reflect the stability of the communication link;

[0034] S404. Obtain the preset fluctuation judgment threshold. If the communication link operation fluctuation coefficient is less than the fluctuation judgment threshold, the link is marked as an available link;

[0035] If the communication link operation fluctuation coefficient is greater than or equal to the fluctuation judgment threshold, the link is marked as a faulty link. When the number of faulty links is greater than the preset fault threshold, a warning instruction is generated.

[0036] To sum up, due to the adoption of the above technical solutions, the beneficial effects of the present invention are:

[0037] The 5G communication network base station node management system based on cloud computing sets up a distributed base station node cluster, densely deploys 5G communication network base stations according to the demand evaluation based on the pedestrian flow data and vehicle flow data in the distribution area to improve the total network capacity, obtains the operation data of all 5G communication network base stations within the regional scope, performs three-dimensional pooling management on the computing, storage, and spectrum resources of the base stations, constructs a traffic prediction model based on historical data at the same time, predicts the resource requirements of real-time traffic, dynamically allocates computing, storage, and network resources to each base station node based on the resource requirement values, obtains each communication resource link, obtains the communication data of the corresponding base station nodes, and evaluates the link transmission based on the communication data to ensure the security and stability during the transmission process. Brief Description of the Drawings

[0038] Figure 1 It shows a schematic diagram of the overall module structure of the present invention. Detailed Description of the Embodiments

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] Embodiment

[0041] As Figure 1 shown, a 5G communication network base station node management system based on cloud computing includes a distributed base station node cluster, a cloud resource computing unit, an edge computing unit, and a resource scheduling unit;

[0042] The distributed base station node cluster is composed of multiple radio access units that support the 5G NR standard, and conducts demand assessment based on the pedestrian flow data and vehicle flow data in the distribution area, and densely deploys 5G communication network base stations according to the demand assessment results to improve the total capacity of the network;

[0043] The specific process of densely deploying 5G communication network base stations according to the demand assessment results is as follows:

[0044] S101. Obtain the distribution area of the 5G communication network base station and calculate the area of the region;

[0045] S102. Obtain the normal pedestrian flow and normal vehicle flow in the distribution area. The pedestrian flow data specifically includes the maximum pedestrian flow, normal pedestrian flow, and their corresponding time periods. The vehicle flow data specifically includes the maximum vehicle flow, normal vehicle flow, and their corresponding time periods;

[0046] S103. Calculate the maximum flow demand based on the maximum pedestrian flow data and maximum vehicle flow data, set the large flow demand area according to the corresponding time period, set the number of 5G communication network base stations according to the maximum flow demand, and perform uniform density distribution settings according to the area of the region to obtain the coordinates of each flow base station node;

[0047] S104. Calculate the normal flow demand based on the normal pedestrian flow data and normal vehicle flow data, set the normal flow demand area according to the corresponding time period, and select normal base station nodes and peak standby base station nodes from the coordinates of each flow base station node according to the regional distribution.

[0048] The base station node is also provided with a breathing control module, which is used to adaptively adjust the mechanism according to the state switching threshold and at the same time follow the power coordination control protocol with neighboring base stations.

[0049] The cloud resource computing unit includes a resource management module and a demand acquisition module. The resource management module is used to obtain the operation data of all 5G communication network base stations within the regional scope and perform three-dimensional pooling management of the computing, storage, and spectrum resources of the base stations;

[0050] The demand acquisition module is used to obtain the real-time vehicle flow data and real-time pedestrian flow data within the regional scope, and at the same time obtain the peak factor within the region. Based on the historical data, a traffic prediction model is constructed to predict the resource demand of the real-time traffic and obtain the resource demand value and send it to the resource scheduling unit;

[0051] The specific process of obtaining the resource demand value is as follows:

[0052] S201. Obtain historical data. The historical data includes historical vehicle flow data, historical pedestrian flow data, and the corresponding time nodes. Calculate the real-time traffic demand according to the base station traffic demand calculation formula, and integrate the real-time traffic demand and time nodes as training samples. Divide the generated training samples into a training set and a test set according to the ratio of 8:2;

[0053] The base station traffic demand calculation formula is specifically: base station traffic demand = (real-time vehicle flow data + real-time pedestrian flow data) × average traffic per user × peak factor;

[0054] S202. Download the weight file and load it onto the corresponding network to initialize the transfer network parameters, and determine the number of hidden layer nodes of the BP neural network model according to the number of samples in the training set;

[0055] S203. Modify the last fully connected layer of the network, keep the input unchanged, set the output to the resource demand value, initialize the weights of the last layer, use the gradient descent algorithm for learning, and use a fixed step size decay to optimize the training parameters. Retrain the entire network to obtain a traffic prediction model;

[0056] S204. During the training process, randomly and non-repeatedly extract small batches of training samples from the training set. After finishing all the training samples in the training set is one training cycle, iterate to a certain number of cycles to complete the training, and then use the test set to evaluate the effect of the traffic prediction model;

[0057] S205. Substitute the real-time vehicle flow data and real-time pedestrian flow data into the traffic prediction model to obtain the resource demand value.

[0058] The resource scheduling unit is used to obtain and process the resource demand value, and dynamically allocate computing, storage, and network resources to each base station node based on the resource demand value to obtain each communication resource link;

[0059] The specific process of obtaining each communication resource link is as follows:

[0060] S301. Obtain the status information of each communication base node, and generate a base station status prediction matrix through the digital twin engine;

[0061] S302. Use the federated learning engine to aggregate multi-base station local models to generate a global policy;

[0062] S303. Solve the constrained optimization problem through the resource scheduling unit:

[0063] where U j is the user utility function, N is the number of users, that is, the sum of real-time vehicle flow data and real-time pedestrian flow data, r j is the preset allocated resource amount, and R total is the maximum traffic limit value of the base station.

[0064] The edge computing node is used to obtain each communication resource link, obtain the communication data of the corresponding base station nodes, perform link transmission evaluation based on the communication data, perform local traffic shaping and fault pre-judgment according to the evaluation results, and generate a warning instruction according to the fault pre-judgment results.

[0065] The specific process of performing link transmission evaluation based on communication data is as follows:

[0066] S401. Collect the communication data of the base station nodes. The communication data includes signal strength Ei, traffic value Qi, and signal-to-noise ratio dBi, and calculate the interference influence index Ui according to the communication data: where i = 1, 2, 3,..., n, and n is the number of communication data. Among them is the standard signal strength, is the standard traffic value, is the standard signal-to-noise ratio, and α, β, and γ are preset proportionality coefficients. The communication quality of the link is evaluated through the interference influence index. The larger the interference influence index, the worse the communication quality of the link, and vice versa, the smaller the interference influence index, the better the communication quality of the link;

[0067] S402. Collect the link operation stability information. The link operation stability information includes transmission signal power Wi, packet loss rate Hi, and throughput Di;

[0068] S403. Obtain the interference influence index and the link operation stability information, and calculate the communication link operation fluctuation coefficient Yi according to the following formula: where e1, e2, and e3 are preset weight coefficients, and the communication link operation fluctuation coefficient is used to reflect the stability of the communication link;

[0069] S404. Obtain a preset fluctuation judgment threshold. If the operation fluctuation coefficient of the communication link is less than the fluctuation judgment threshold, the link is marked as an available link;

[0070] If the operation fluctuation coefficient of the communication link is greater than or equal to the fluctuation judgment threshold, the link is marked as a faulty link. When the number of faulty links is greater than the preset fault threshold, a warning instruction is generated.

[0071] The present invention improves the total capacity of the network by setting up a distributed base station node cluster, densely deploying 5G communication network base stations according to the pedestrian flow data and vehicle flow data in the distribution area for demand assessment, obtaining the operation data of all 5G communication network base stations within the regional scope, performing three-dimensional pooling management on the computing, storage, and spectrum resources of the base stations, constructing a traffic prediction model based on historical data to predict the resource requirements of real-time traffic, dynamically allocating computing, storage, and network resources to each base station node based on the resource requirement values to obtain each communication resource link, obtaining the communication data of the corresponding base station nodes, and performing link transmission evaluation based on the communication data, which can not only meet the traffic requirements but also ensure the security and stability during the transmission process.

[0072] The setting of the threshold value is for the convenience of comparison. Regarding the size of the threshold value, it depends on the amount of sample data and the base quantity set by those skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantified value is not affected.

[0073] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation;

[0074] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent replacements or changes, should be covered by the protection scope of the present invention.

Claims

1. A base station node management system for a 5G communication network based on cloud computing, characterized in that, It includes a distributed base station node cluster, a cloud resource computing unit, an edge computing unit, and a resource scheduling unit; The distributed base station node cluster is composed of multiple wireless access units supporting the 5G NR standard, and conducts demand assessment based on the pedestrian flow data and vehicle flow data in the distribution area, and densely deploys 5G communication network base stations according to the demand assessment results to improve the total capacity of the network; The cloud resource computing unit includes a resource management module and a demand acquisition module. The resource management module is used to obtain the operation data of all 5G communication network base stations within the regional scope, and perform three-dimensional pooling management of the base station computing, storage, and spectrum resources; The demand acquisition module is used for the real-time vehicle flow data and real-time pedestrian flow data within the regional scope, and at the same time obtains the peak factor within the region, constructs a traffic prediction model based on historical data, predicts the resource demand of the real-time traffic, and sends the resource demand value to the resource scheduling unit; The resource scheduling unit is used to obtain and process the resource demand value, and dynamically allocate computing, storage, and network resources to each base station node based on the resource demand value to obtain each communication resource link; The edge computing node is used to obtain each communication resource link, and obtain the communication data of the corresponding base station nodes, conduct link transmission evaluation based on the communication data, perform local traffic shaping and fault pre-judgment according to the evaluation results, and generate a warning instruction according to the fault pre-judgment result.

2. The 5G communication network base station node management system based on cloud computing according to claim 1, characterized in that, The specific process of densely deploying 5G communication network base stations according to the demand assessment results is as follows: S101. Obtain the distribution area of the 5G communication network base station and calculate the area of the region; S102. Obtain the normal pedestrian flow and normal vehicle flow in the distribution area. The pedestrian flow data specifically includes the maximum pedestrian flow, normal pedestrian flow, and their corresponding time periods. The vehicle flow data specifically includes the maximum vehicle flow, normal vehicle flow, and their corresponding time periods; S103. Calculate the maximum traffic demand according to the maximum pedestrian flow data and maximum vehicle flow data, set the large traffic demand area according to the corresponding time period, set the number of 5G communication network base stations according to the maximum traffic demand, and perform uniform density distribution setting according to the area of the region to obtain the coordinates of each traffic base station node; S104. Calculate the normal traffic demand according to the normal pedestrian flow data and normal vehicle flow data, set the normal traffic demand area according to the corresponding time period, and select normal base station nodes and peak standby base station nodes from the coordinates of each traffic base station node according to the regional distribution situation.

3. The base station node management system for a 5G communication network based on cloud computing according to claim 1, wherein The base station node is also provided with a breathing control module, which is used for the adaptive adjustment mechanism according to the state switching threshold, and at the same time follows the power coordination control protocol with adjacent base stations.

4. The 5G communication network base station node management system based on cloud computing according to claim 1, characterized in that, The specific process of obtaining the resource demand value is as follows: S201. Obtain historical data, which includes historical vehicle flow data, historical pedestrian flow data, and their corresponding time nodes. Calculate the real-time traffic demand according to the base station traffic demand calculation formula, and integrate the real-time traffic demand and time node as a training sample. Divide the generated training sample into a training set and a test set according to the ratio of 8:2; S202. Download the weight file and load it onto the corresponding network to initialize the migration network parameters, and determine the number of hidden layer nodes of the BP neural network model according to the number of samples in the training set; S203. Modify the last fully connected layer of the network, keep the input unchanged, set the output to the resource demand value, initialize the weights of the last layer, use the gradient descent algorithm for learning, and adopt fixed-step decay to optimize the training parameters, and retrain the entire network to obtain a traffic prediction model; S204. During the training process, randomly and non-repeatedly extract small batches of training samples from the training set. After extracting all the training samples in the training set, it is regarded as one training cycle. Iterate to a certain number of cycles to complete the training, and then use the test set to evaluate the effect of the traffic prediction model; S205. Substitute the real-time vehicle flow data and real-time pedestrian flow data into the traffic prediction model to obtain the resource demand value.

5. The base station node management system for a 5G communication network based on cloud computing according to claim 1, wherein The specific process of obtaining each communication resource link is as follows: S301. Obtain the status information of each communication base station node, and generate a base station status prediction matrix through the digital twin engine; S302. Use the federated learning engine to aggregate the local models of multiple base stations to generate a global policy; S303. Solve the constrained optimization problem through the resource scheduling unit: , where U j is the user utility function, N is the number of users, i.e., the sum of real-time vehicle flow data and real-time pedestrian flow data, and r j is the preset allocated resource amount, and R total is the maximum traffic limit value of the base station.

6. The base station node management system for a 5G communication network based on cloud computing according to claim 1, characterized in that, The specific process of link transmission evaluation based on communication data is as follows: S401. Collect the communication data of the base station node. The communication data includes the signal strength Ei, the traffic value Qi, and the signal-to-noise ratio dBi. Calculate the interference impact index Ui according to the communication data: , i = 1, 2, 3, …, n, where n is the number of communication data, and is the standard signal strength, is the standard traffic value, is the standard signal-to-noise ratio, and α, β, and γ are preset proportionality coefficients. Evaluate the communication quality of the link through the interference impact index; S402. Collect the link operation stability information, where the link operation stability information includes the transmitted signal power Wi, packet loss rate Hi, and throughput Di; S403. Obtain the interference influence index and the link operation stability information, and calculate the communication link operation fluctuation coefficient Yi according to the following formula: where e1, e2, and e3 are preset weight coefficients, and the communication link operation fluctuation coefficient is used to reflect the stability of the communication link; S404. Obtain the preset fluctuation judgment threshold. If the communication link operation fluctuation coefficient is less than the fluctuation judgment threshold, the link is marked as an available link; If the communication link operation fluctuation coefficient is greater than or equal to the fluctuation judgment threshold, the link is marked as a faulty link. When the number of faulty links is greater than the preset fault threshold, a warning instruction is generated.

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