CN111107651B - Method and device for scheduling wireless resources - Google Patents
Method and device for scheduling wireless resources Download PDFInfo
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- CN111107651B CN111107651B CN201911379078.8A CN201911379078A CN111107651B CN 111107651 B CN111107651 B CN 111107651B CN 201911379078 A CN201911379078 A CN 201911379078A CN 111107651 B CN111107651 B CN 111107651B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application provides a method and a device for scheduling wireless resources, relates to the technical field of communication, and is used for dynamically scheduling the wireless resources for a terminal and improving the resource utilization rate. The method comprises the following steps: the method comprises the steps that the type and the size of a wireless resource pool are pre-configured by access network equipment, and service characteristic parameters of a terminal are obtained, wherein the service characteristic parameters are used for reflecting the characteristics of a service executed by the terminal; and finally, the access network equipment adjusts the wireless resource pool according to the wireless resources currently allocated to the terminal and the demand predicted value. The application is applied to the scheduling of the wireless resources by the access network equipment.
Description
Technical Field
The present application relates to the field of communications, and in particular, to a method and an apparatus for scheduling radio resources.
Background
The large bandwidth, the large connection, the low time delay and the high reliability form three 5G application scenes. In order to fully support these typical application scenarios, the schedule-free transmission is regarded as a key technology of 5G by the third generation partnership project (3 GPP) with advantages of low signaling overhead, low time delay, and low power consumption, and is highly regarded as important in domestic and foreign research and 3GPP standard definition. However, since multiple terminals compete for access simultaneously, the probability of terminal access failure potentially becomes high.
At present, the method of resource grouping is adopted to solve the problems. However, in the present phase, the resource grouping method is based on static parameters, and does not consider that the use state of the terminal device is changed, so that the result of resource grouping is not optimal. Meanwhile, the resource grouping method in the prior art does not consider the service characteristics of the user industry data bearer, and the allocation of wireless resources is not enough to guarantee the requirement of service quality.
Therefore, in the scheduling-free transmission, an appropriate solution is needed in the industry for the current situation that the resource utilization rate is low due to the fact that the current resource allocation method based on the static parameters does not fully utilize the dynamic parameters.
Disclosure of Invention
The application provides a method and a device for scheduling wireless resources, which are used for solving the problem of low resource utilization rate caused by the fact that a resource allocation method based on static parameters does not fully utilize dynamic parameters at the present stage.
In order to achieve the purpose, the following scheme is adopted in the application:
in a first aspect, the present application provides a method for scheduling radio resources, including: the method comprises the steps that access network equipment obtains service characteristic parameters of each terminal in P terminals, the service characteristic parameters of the terminals are used for reflecting the characteristics of services executed by the terminals, the P terminals are terminals connected with the access network equipment, and P is a positive integer; the access network equipment inputs the service characteristic parameters of each terminal in the P terminals into a trained prediction model, and determines a demand prediction value of each terminal in the P terminals, wherein the demand prediction value of each terminal comprises prediction values corresponding to M wireless resource pools, the prediction values corresponding to the wireless resource pools are used for reflecting the demands of the terminals for resources in the wireless resource pools in a future period of time, the M wireless resource pools are pre-configured by the access network equipment, and M is a positive integer; and the access network equipment determines whether to adjust the M wireless resource pools according to the demand predicted values of the P terminals.
Based on the technical scheme, the access network equipment configures the type and the size of the wireless resource pool in advance, acquires the service characteristic parameters of the terminal, inputs the service characteristic parameters into a trained prediction model to determine a demand predicted value, and finally adjusts the wireless resource pool according to the wireless resources currently allocated to the terminal and the demand predicted value. Therefore, the access network equipment schedules the wireless resources based on the characteristics of the terminal bearing service, dynamically optimized and high-reliability service guarantee, so that the terminal can obtain the optimal wireless resources, the resource utilization rate is improved, and the service quality is guaranteed.
In one possible design, the determining, by the access network device, whether to adjust M radio resource pools according to the predicted demand values of P terminals includes: the access network equipment determines the predicted values corresponding to the P target wireless resource pools according to the demand predicted values of the P terminals, the predicted values corresponding to the P target wireless resource pools correspond to the P terminals one by one, and the target wireless resource pool belongs to any one of the M wireless resource pools. And the access network equipment determines the required value of the P terminals to the target wireless resource pool according to the predicted values corresponding to the P target wireless resource pools, wherein the required value of the P terminals to the target wireless resource pool is equal to the sum of the predicted values corresponding to the P target wireless resource pools. The access network equipment determines the total demand value of the P terminals to the wireless resources according to the demand value of the P terminals to each target wireless resource pool in the M wireless resource pools, wherein the total demand value of the P terminals to the wireless resources is equal to the sum of the demand values of the P terminals to each target wireless resource pool in the M wireless resource pools. The access network equipment presets a first threshold. If the total required value of the P terminals to the wireless resources is greater than or equal to a first threshold value, the access network equipment adjusts and adjusts the M wireless resource pools; or, if the total required value of the P terminals for the radio resources is smaller than the first threshold, the access network device does not adjust the M radio resource pools.
In one possible design, the traffic characteristic parameters include one or more of the following parameters: the method comprises the following steps of connection time, data continuous transmission duration, the number of times of applying for wireless resources in unit time, the number of retransmission times in unit time, the average rate and peak rate in unit time, the data transmission quantity in unit time and the QoS requirement of a terminal on the wireless resources of access network equipment.
In one possible design, the predictive model is constructed based on a least squares method, a polynomial fitting method, or a neural network algorithm.
In a possible design, if the prediction model is constructed based on a neural network, the method further comprises: the access network equipment acquires a training set, wherein the training set comprises a plurality of sample data, and each sample data comprises a service characteristic parameter of a terminal in unit time; mapping the training set into required values of the terminal for different wireless resource pools; and constructing a prediction model according to the required values of the terminal to different wireless resource pools.
In a second aspect, the present application provides an access network device, including: an obtaining module, configured to obtain service characteristic parameters of each of P terminals, where the service characteristic parameters of the terminals are used to reflect characteristics of a service executed by the terminals, and the P terminals are terminals connected to the access network device. A processing module, configured to input the service characteristic parameter of each of the P terminals into a trained prediction model, and determine a demand prediction value of each of the P terminals, where the demand prediction value of each terminal includes prediction values corresponding to M radio resource pools, where the prediction value corresponding to a radio resource pool is used to reflect a demand of the terminal for resources in the radio resource pool in a future period of time, where the M radio resource pools are pre-configured by the access network device, and M is a positive integer; and determining whether to adjust the M resource pools according to the demand predicted values of the P terminals.
In one possible design, the processing module is further configured to determine predicted values corresponding to P target radio resource pools according to the demand predicted values of P terminals, where the predicted values corresponding to the P target radio resource pools correspond to the P terminals one to one, and each target radio resource pool belongs to any one of the M radio resource pools. And the processing module is further used for determining the required value of the P terminals to the target wireless resource pool according to the predicted values corresponding to the P target wireless resource pools, wherein the required value of the P terminals to the target wireless resource pool is equal to the sum of the predicted values corresponding to the P target wireless resource pools. And the processing module is also used for determining the total required value of the P terminals for the wireless resources according to the required value of the P terminals for each target wireless resource pool in the M wireless resource pools, wherein the total required value of the P terminals for the wireless resources is equal to the sum of the required values of the P terminals for each target wireless resource pool in the M wireless resource pools. The processing module is further used for presetting a first threshold. The processing module is further used for adjusting the number of resources in each of the M wireless resource pools when the total required value of the P terminals to the wireless resources is greater than or equal to a first threshold value; the method is used for not adjusting the quantity of the resources in each of the M wireless resource pools when the total demand value of the P terminals to the wireless resources is smaller than a first threshold value.
In one possible design, the traffic characteristic parameters include one or more of the following parameters: the method comprises the following steps of connection time, data continuous transmission duration, the number of times of applying for wireless resources in unit time, the number of retransmission times in unit time, the average rate and peak rate in unit time, the data transmission quantity in unit time and the QoS requirement of a terminal on the wireless resources of access network equipment.
In one possible design, the predictive model is constructed based on a least squares method, a polynomial fitting method, or a neural network algorithm.
In one possible design, the obtaining module is further configured to obtain a training set; the training set comprises a plurality of sample data, and each sample data comprises the service characteristic parameters of the terminal in unit time. And the processing module is also used for mapping the training set into the required values of the terminal to different wireless resource pools. And the processing module is also used for constructing a prediction model according to the required values of the terminal to different wireless resource pools.
In a third aspect, the present application provides an access network device, including: a processor and a communication interface; the communication interface is coupled to a processor for executing a computer program or instructions for implementing the method for scheduling of radio resources as described in the first aspect and any possible implementation form of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to perform the method for scheduling radio resources described in the first aspect and any one of the possible implementation manners of the first aspect.
In a fifth aspect, the present application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the method for scheduling radio resources described in the first aspect and any one of the possible implementations of the first aspect.
In a sixth aspect, the present application provides a chip comprising a processor and a communication interface, the communication interface being coupled to the processor, the processor being configured to execute a computer program or instructions to implement the method for scheduling radio resources as described in the first aspect and any possible implementation manner of the first aspect.
Drawings
Fig. 1 is a flowchart illustrating a method for scheduling radio resources according to an embodiment of the present disclosure;
fig. 2 is a flowchart illustrating another method for scheduling radio resources according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an access network device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another access network device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship. For example, A/B may be understood as either A or B.
The terms "first" and "second" in the description and claims of the present application are used for distinguishing between different objects and not for describing a particular order of the objects. For example, the first edge service node and the second edge service node are used for distinguishing different edge service nodes, and are not used for describing the characteristic sequence of the edge service nodes.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to the listed steps or modules but may alternatively include other steps or modules not listed or inherent to such process, method, article, or apparatus.
In addition, in the embodiments of the present application, words such as "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts in a concrete fashion.
In order to facilitate understanding of the technical solutions of the present application, some technical terms are described below.
1. Wireless resource pool
The wireless resource pool is formed by grouping the resources for free scheduling by the access network equipment. The number of the wireless resource pools can be determined according to actual conditions. Typically, the wireless resource pool can be divided into a wireless resource pool with a long connection characteristic, a wireless resource pool with a data burst characteristic and a general wireless resource pool. The wireless resource pool with the long connection characteristic is used for services which need continuous and large-amount data transmission, such as video services and large-data-volume data downloading services, and the services have high requirements on bandwidth and time jitter; the wireless resource pool with the data burst characteristic is used for services with small single data volume but high transmission frequency, such as industrial control services and services issued by unmanned commands, and the services have high requirements on transmission delay and reliability.
2. Neural network algorithm
Neural Network Algorithms (NNAs) are mathematical or computational models that mimic the structure and function of biological neural networks. Neural network algorithms are computed from a large number of artificial neuron connections. In most cases, the artificial neural network can change the internal structure on the basis of external information, and is an adaptive system. Modern neural network algorithms are a non-linear statistical data modeling tool that is often used to model complex relationships between inputs and outputs, or to explore patterns in data.
The neural network algorithm is an operational model and is composed of a large number of nodes and mutual connection among the nodes. Each node represents a particular output function, called the excitation function or activation function. The connection between every two nodes represents a weighted value, called weight, for the signal passing through the connection, which is equivalent to the memory of an artificial neural network. The output of the network is different according to the connection mode of the network, the weight value and the excitation function. The network itself is usually an approximation to some algorithm or function in nature, and may also be an expression of a logic strategy.
Its construction idea is inspired by the operation of biological (human or other animal) neural network function. Artificial neural networks are usually optimized by a learning method based on mathematical statistics, so that they are a practical application of mathematical statistics, and a large number of local structural spaces that can be expressed by functions can be obtained by a standard mathematical method of statistics. This approach has advantages over formal logistic reasoning algorithms.
3. Access network device
And the access network equipment is used for connecting with the terminal so as to enable the terminal to access the wireless network. In the application, the access network device pre-configures the radio resources, and divides the radio resources into M radio resource pools, where M is a positive integer. The attributes of each radio resource pool include: the type of the radio resource pool, the size of the radio resource pool (i.e. the amount of resources comprised by the resource pool).
The access network equipment acquires the service type of the transmission data bearer of the terminal in a previous period of time, and then the access network equipment analyzes the requirements of the service types on network transmission bandwidth, connection duration, transmission delay, time jitter, peak rate and the like according to the service type of the terminal, and determines the type and size of a wireless resource pool according to the requirements.
Illustratively, the type of the access network device configuring the radio resource pool includes: a radio resource pool of a long connection characteristic, a radio resource pool of a data burst characteristic, and a general radio resource pool. Then, the resources in the wireless resource pool with the long connection characteristic of the access network device are allocated to the services which need continuous and large data transmission, the resources in the wireless resource pool with the data burst characteristic are allocated to the services which have small single data volume but high transmission frequency, and the resources in the general wireless resource pool are allocated to the conventional common services.
The technical solution provided by the present application is specifically explained below with reference to the drawings of the specification.
As shown in fig. 1, a method for scheduling radio resources according to an embodiment of the present application includes the following steps:
s101, the access network equipment acquires the service characteristic parameters of each of the P terminals.
The service characteristic parameter is used for reflecting the characteristics of the service executed by the terminal. The traffic characteristic parameters may comprise one or more of the following parameters: the method comprises the following steps of connection time, data continuous transmission duration, the number of times of applying for wireless resources in unit time, the number of retransmission times in unit time, the average rate and peak rate in unit time, the data transmission quantity in unit time and the QoS requirement of a terminal on the wireless resources of access network equipment.
S102, the access network equipment inputs the service characteristic parameters of each of the P terminals into the trained prediction model, and determines the demand prediction value of each of the P terminals.
Wherein the demand forecast value is used for reflecting the demand of the terminal for the wireless resource in a future period of time. The demand predicted value of each terminal comprises predicted values corresponding to M wireless resource pools, and the predicted value corresponding to each wireless resource pool is used for reflecting the demand of the terminal for the resources in the wireless resource pool in a future period.
Optionally, the access network device constructs the prediction model based on a least square method, a polynomial fitting method, or a neural network algorithm.
S103, the access network equipment determines whether to adjust the M wireless resource pools according to the demand forecast values of the P terminals.
Optionally, the access network device determines the first threshold according to the utilization rate of each of the M radio resource pools, the success rate of the terminal radio access, and the number of times of data retransmission of the terminal user. The first threshold is used for the access network device to determine whether to adjust the M wireless resource pools. Illustratively, the access network device sets the first threshold to 90% of the total amount of radio resources of the access network device.
In a possible implementation manner, the access network device presets a first threshold, then, the access network device obtains a total required value of the P terminals for the wireless resources according to the required predicted values of the P terminals, and finally, the access network device compares the total required value with the first threshold, and determines whether to adjust the M wireless resource pools according to a comparison result.
Optionally, the access network device determines the predicted values corresponding to the P target radio resource pools according to the predicted values of the demands of the P terminals. The predicted values corresponding to the P target wireless resource pools correspond to the P terminals one by one, and the target wireless resource pool belongs to any one of the M wireless resource pools.
Optionally, the access network device determines, according to the predicted values corresponding to the P target radio resource pools, the required values of the P terminals to the target radio resource pools. The demand value of the P terminals to the target wireless resource pool is equal to the sum of the predicted values corresponding to the P target wireless resource pools.
Optionally, the access network device determines a total required value of the P terminals for the radio resources according to the required value of the P terminals for each target radio resource pool in the M radio resource pools. Wherein the total demand value of the P terminals for the radio resources is equal to the sum of the demand values of the P terminals for each of the M pools of radio resources.
Optionally, if the total required value of the P terminals for the radio resources is greater than or equal to the first threshold, the access network device adjusts the M radio resource pools; and if the total required value of the P terminals to the wireless resources is smaller than the first threshold value, the access network equipment does not adjust the M wireless resource pools.
Optionally, after the access network device determines to adjust the M radio resource pools, the adjusting, by the access network device, the M radio resource pools includes:
(1) and the access network equipment adjusts the amount of the wireless resources allocated to different terminals.
Optionally, the access network device reduces or even cancels the amount of the wireless resources currently allocated to the low-priority terminal, so as to meet the requirement of the high-priority terminal on the wireless resources and ensure that the service of the high-priority terminal can be normally executed. When the terminal is connected to the access network device for the first time, the priority of the terminal is configured by the access network device according to the identifier of the terminal.
Optionally, the access network device dynamically adjusts the priority level of each terminal according to the historical requirement of the service of each terminal on the radio resource in the actual execution process.
Illustratively, if a user using a terminal enters into a service agreement with an operator for providing a large network bandwidth in a time period from 22 hours a day to 7 days the next time, the access network device increases the priority of the terminal in the time period from 22 hours a day to 7 days the next time, and decreases the priority of the terminal in the rest time periods of each day.
(2) And the access network equipment adjusts the proportion among the different types of wireless resource pools.
Optionally, the access network device presets a second threshold. The second threshold is specifically the number of times of failure of the terminal to apply for the radio resource in a unit time assumed by the access network device.
Illustratively, the access network device counts the number of times of failure of the terminal to apply for the radio resource in the actual service execution process. Then, the access network equipment compares the failure times of the terminal applying for the wireless resources in the actual service execution process with a second threshold, and if the failure times of the terminal applying for the wireless resources in the actual service execution process are greater than or equal to the second threshold, the access network equipment adjusts the proportion among different types of wireless resource pools; if the number of times of failure of the terminal to apply for the radio resources in the actual service execution process is less than the second threshold, the access network device does not adjust the proportion between the radio resource pools of different types.
Optionally, when the overall utilization rate of the radio resource of the access network device is different, the access network device sets a second threshold with a different value. Illustratively, when the overall utilization rate of the radio resources of the access network device is low, the access network device sets a second threshold value X with a larger value1(ii) a When the overall utilization rate of the wireless resources of the access network equipment is high, the access network equipment sets a second threshold value X with a small value2. Wherein, X1≥1.5×X2。
Therefore, the access network equipment can avoid the access network equipment from frequently adjusting the wireless resources by dynamically setting the second threshold value, thereby reducing the system overhead, simultaneously keeping the type and the size of the wireless resource pool stable, and being beneficial to improving the resource utilization rate.
Based on the technical scheme, the access network equipment configures the type and the size of the wireless resource pool in advance, acquires the service characteristic parameters of the terminal, inputs the service characteristic parameters into a trained prediction model to determine a demand predicted value, and finally adjusts the wireless resource pool according to the wireless resources currently allocated to the terminal and the demand predicted value. According to the technical scheme, the dynamic change condition of the terminal for the requirements of the resources in the wireless resource pools of different types can be acquired in real time, the wireless resources required by the terminal are accurately predicted, and then the access network equipment dynamically adjusts the wireless resource pools according to the prediction result, so that the terminal obtains the optimal wireless resources, and the resource utilization rate is improved. Under the condition of the same network resource, the technical scheme can provide higher resource utilization rate and higher service quality.
As shown in fig. 2, for an example that a prediction model is constructed based on a neural network, the method for scheduling a wireless resource provided in the embodiment of the present application further includes the following steps after step S101:
s201, the access network equipment acquires a training set.
The training set comprises a plurality of sample data, and each sample data comprises the service characteristic parameters of the terminal in a unit time.
S202, the access network equipment maps the training set into the requirement values of the terminal for different wireless resource pools.
Optionally, the access network device presets a mapping relationship table. The mapping relation table is used for reflecting the corresponding relation between the service characteristic parameters of the terminals in the training set and different wireless resource pools.
In a possible implementation manner, the access network device maps the training set to the required values of the terminal for different radio resource pools according to the mapping relationship.
Illustratively, the types of the radio resource pool configured by the access network device include: a radio resource pool of a long connection characteristic, a radio resource pool of a data burst characteristic, and a general radio resource pool. The access network device maps the training set as: the terminal's demand value for the pool of radio resources for long connection characteristics, the terminal's demand value for the pool of radio resources for data burst characteristics, and the terminal's demand value for the pool of general radio resources.
S203, the access network equipment constructs a prediction model according to the required values of the terminal to different wireless resource pools.
Optionally, the access network device inputs the required values of the terminal for different wireless resource pools in a plurality of unit times based on a multi-layer neural network algorithm, and finally constructs a prediction model.
Based on the technical scheme, the access network equipment can construct a prediction model according to a neural network algorithm, the prediction model is used for the access network equipment to predict the requirement of the terminal for the wireless resources in a future period of time, a requirement predicted value is obtained, and then the access network equipment adjusts the wireless resources allocated to the terminal according to the wireless resources currently allocated to the terminal and the requirement predicted value.
In the embodiment of the present application, the access network device may be divided into the functional modules or the functional units according to the above method examples, for example, each functional module or functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module may be implemented in a form of hardware, or may be implemented in a form of a software functional module or a functional unit. The division of the modules or units in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Fig. 3 is a schematic diagram of a possible structure of an access network device according to an embodiment of the present application, where the schematic diagram includes:
an obtaining module 101, configured to obtain service characteristic parameters of each of P terminals, where the service characteristic parameters of the terminals are used to reflect characteristics of a service executed by the terminals, and the P terminals are terminals connected to the access network device.
A processing module 102, configured to input the service characteristic parameter of each of the P terminals into a trained prediction model, and determine a demand prediction value of each of the P terminals, where the demand prediction value of each terminal includes prediction values corresponding to M radio resource pools, where the prediction value corresponding to the radio resource pool is used to reflect a demand of the terminal for resources in the radio resource pool in a future period of time, where the M radio resource pools are pre-configured by the access network device, and M is a positive integer; and determining whether to adjust the M resource pools according to the demand predicted values of the P terminals.
The processing module 102 is further configured to determine predicted values corresponding to P target radio resource pools according to the demand predicted values of the P terminals, where the predicted values corresponding to the P target radio resource pools correspond to the P terminals one to one, and each target radio resource pool belongs to any one of the M radio resource pools.
The processing module 102 is further configured to determine, according to the predicted values corresponding to the P target wireless resource pools, a required value of the P terminals to the target wireless resource pools, where the required value of the P terminals to the target wireless resource pools is equal to the sum of the predicted values corresponding to the P target wireless resource pools.
The processing module 102 is further configured to determine a total required value of the radio resources for the P terminals according to the required value of the P terminals for each target radio resource pool in the M radio resource pools, where the total required value of the radio resources for the P terminals is equal to the sum of the required values of the P terminals for each target radio resource pool in the M radio resource pools.
The processing module 102 is further configured to preset a first threshold.
The processing module 102 is further configured to, when the total required value of the P terminals for the radio resources is greater than or equal to a first threshold, adjust the number of resources in each of the M radio resource pools; the method is used for not adjusting the quantity of the resources in each of the M wireless resource pools when the total demand value of the wireless resources from the P terminals is less than a first threshold value.
Optionally, the obtaining module 101 is further configured to obtain a training set; the training set includes a plurality of sample data, each sample data including a traffic characteristic parameter of the terminal in a unit time. The processing module 102 is further configured to map the training set to requirement values for different radio resource pools. The processing module 102 is further configured to construct a prediction model according to the required values of the terminal for different wireless resource pools.
Fig. 4 is a schematic diagram of a possible structure of an access network device according to an embodiment of the present application, where the schematic diagram includes:
a processor 202 for controlling and managing the actions of the access network equipment, e.g., performing the steps performed by the processing module 102 described above, and/or other processes for performing the techniques described herein.
The processor 202 may be various illustrative logical blocks, modules, and circuits described above that implement or perform the functions described in connection with the disclosure. The processor may be a central processing unit, general purpose processor, digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, among others.
Optionally, the access network device may further include a communication interface 203, a memory 201, and a bus 204, where the communication interface 203 is used to support the access network device to communicate with other network entities. A memory 201 is used to store program codes and data for the access network equipment.
Wherein the memory 201 may be a memory in an access network device, which may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk; the memory may also comprise a combination of the above types of memory.
The bus 204 may be an Extended Industry Standard Architecture (EISA) bus or the like. The bus 204 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 4, but this does not indicate only one bus or one type of bus.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus, and the module described above, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
Embodiments of the present application provide a computer program product including instructions, which when run on a computer, cause the computer to execute the method for identifying a node of an internet of things according to the foregoing method embodiments.
An embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the network device executes the instructions, the network device executes each step executed by the network device in the method flow shown in the foregoing method embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, and a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM), registers, a hard disk, an optical fiber, a portable Compact disk Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any other form of computer-readable storage medium, in any suitable combination, or as appropriate in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for scheduling radio resources, the method comprising:
the method comprises the steps that access network equipment obtains service characteristic parameters of each terminal in P terminals, the service characteristic parameters of the terminals are used for reflecting the characteristics of services executed by the terminals, the P terminals are terminals connected with the access network equipment, and P is a positive integer;
the access network equipment inputs the service characteristic parameters of each terminal in the P terminals into a trained prediction model, and determines a demand prediction value of each terminal in the P terminals, wherein the demand prediction value of each terminal comprises prediction values corresponding to M wireless resource pools, the prediction values corresponding to the wireless resource pools are used for reflecting the demands of the terminals on resources in the wireless resource pools in the future period of time, the M wireless resource pools are pre-configured by the access network equipment, and M is a positive integer;
the access network equipment determines whether to adjust M wireless resource pools according to the demand predicted values of the P terminals;
the access network equipment determines whether to adjust M wireless resource pools according to the demand predicted values of the P terminals, and the method comprises the following steps:
the access network equipment determines predicted values corresponding to P target wireless resource pools according to the demand predicted values of the P terminals, the predicted values corresponding to the P target wireless resource pools correspond to the P terminals one by one, and the target wireless resource pools belong to any one of the M wireless resource pools;
the access network equipment determines the required value of the P terminals to the target wireless resource pool according to the predicted values corresponding to the P target wireless resource pools, wherein the required value of the P terminals to the target wireless resource pool is equal to the sum of the predicted values corresponding to the P target wireless resource pools;
the access network equipment determines a total demand value of the P terminals for the wireless resources according to the demand value of the P terminals for each target wireless resource pool in the M wireless resource pools, wherein the total demand value of the P terminals for the wireless resources is equal to the sum of the demand values of the P terminals for each target wireless resource pool in the M wireless resource pools;
the access network equipment presets a first threshold;
if the total required value of the P terminals to the wireless resources is greater than or equal to a first threshold value, the access network equipment adjusts M wireless resource pools;
or
And if the total required value of the P terminals to the wireless resources is smaller than a first threshold value, the access network equipment does not adjust the M wireless resource pools.
2. The method of claim 1, wherein the traffic characteristic parameters comprise one or more of the following parameters: the method comprises the following steps of connection time, data continuous transmission duration, the number of times of applying for wireless resources in unit time, the number of retransmission times in unit time, the average rate and peak rate in unit time, the data transmission quantity in unit time and the QoS (quality of service) requirement of a terminal on the wireless resources of the access network equipment.
3. The method of claim 1 or 2, wherein the prediction model is constructed based on a least square method, a polynomial fitting method, or a neural network algorithm.
4. The method of claim 3, wherein if the prediction model is constructed based on a neural network algorithm, the method further comprises:
acquiring a training set, wherein the training set comprises a plurality of sample data, and each sample data comprises a service characteristic parameter of one service unit of the terminal in unit time;
mapping the training set into required values of the terminal for different wireless resource pools;
and constructing the prediction model according to the demand values of the terminal to different wireless resource pools.
5. An access network device, characterized in that the access network device comprises:
an obtaining module, configured to obtain service characteristic parameters of each of P terminals, where the service characteristic parameters of the terminals are used to reflect characteristics of a service executed by the terminals, and the P terminals are terminals connected to the access network device;
a processing module, configured to input the service characteristic parameter of each of the P terminals into a trained prediction model, and determine a demand prediction value of each of the P terminals, where the demand prediction value of each terminal includes prediction values corresponding to M radio resource pools, where the prediction value corresponding to a radio resource pool is used to reflect a demand of the terminal for resources in the radio resource pool in a future period of time, where the M radio resource pools are pre-configured by the access network device, and M is a positive integer; the resource pool adjusting module is used for determining whether to adjust the M resource pools according to the demand predicted values of the P terminals;
the processing module is further configured to determine predicted values corresponding to P target radio resource pools according to the demand predicted values of the P terminals, where the predicted values corresponding to the P target radio resource pools correspond to the P terminals one to one, and the target radio resource pool belongs to any one of the M radio resource pools;
the processing module is further configured to determine, according to the predicted values corresponding to the P target wireless resource pools, required values of the P terminals to the target wireless resource pools, where the required values of the P terminals to the target wireless resource pools are equal to the sum of the predicted values corresponding to the P target wireless resource pools;
the processing module is further configured to determine a total required value of the P terminals for the wireless resources according to the required value of the P terminals for each target wireless resource pool in the M wireless resource pools, where the total required value of the P terminals for the wireless resources is equal to the sum of the required values of the P terminals for each target wireless resource pool in the M wireless resource pools;
the processing module is further used for presetting a first threshold;
the processing module is further configured to adjust the number of resources in each of the M wireless resource pools when the total required value of the P terminals for the wireless resources is greater than or equal to a first threshold; the method is used for not adjusting the quantity of the resources in each of the M wireless resource pools when the total demand value of the P terminals to the wireless resources is smaller than a first threshold value.
6. The access network device of claim 5, wherein the traffic characteristic parameters include one or more of the following parameters: the method comprises the following steps of connection time, data continuous transmission duration, the number of times of applying for wireless resources in unit time, the number of retransmission times in unit time, the average rate and peak rate in unit time, the data transmission quantity in unit time and the QoS requirement of a terminal on the wireless resources of access network equipment.
7. The access network device of claim 5 or 6, wherein the predictive model is constructed based on a least squares method, a polynomial fitting method, or a neural network algorithm.
8. The access network device of claim 7, wherein if the predictive model is constructed based on a neural network algorithm, the access network device further comprises:
the acquisition module is further used for acquiring a training set; the training set comprises a plurality of sample data, and each sample data comprises a service characteristic parameter of one service unit of the terminal in unit time;
the processing module is further configured to map the training set to required values of the terminal for different wireless resource pools;
the processing module is further configured to construct the prediction model according to the demand values of the terminal for different wireless resource pools.
9. An access network device, comprising: a processor and a communication interface; the communication interface is coupled to the processor, which is configured to execute a computer program or instructions to implement the method for scheduling radio resources as claimed in any of the preceding claims 1-4.
10. A computer-readable storage medium having instructions stored therein, wherein when the instructions are executed by a computer, the computer performs the method for scheduling radio resources according to any one of claims 1 to 4.
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| CN113645642B (en) * | 2021-08-16 | 2023-08-15 | 北京遥感设备研究所 | Radio resource allocation prediction method and system |
| CN115734229A (en) * | 2021-08-31 | 2023-03-03 | 上海华为技术有限公司 | Resource pre-estimation method and related equipment |
| CN116033584B (en) * | 2023-02-03 | 2023-10-20 | 阿里巴巴（中国）有限公司 | Air interface resource scheduling method, network access equipment and communication network |
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