CN114554509A - Wireless resource optimization method, device and computer readable storage medium - Google Patents

Wireless resource optimization method, device and computer readable storage medium Download PDF

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Publication number
CN114554509A
CN114554509A CN202011340530.2A CN202011340530A CN114554509A CN 114554509 A CN114554509 A CN 114554509A CN 202011340530 A CN202011340530 A CN 202011340530A CN 114554509 A CN114554509 A CN 114554509A
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China
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cell
resource optimization
radio resource
configuring
related information
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Chinese (zh)
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解宇瑄
孙奇
段然
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China Mobile Communications Group Co Ltd
China Mobile Communications Ltd Research Institute
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China Mobile Communications Group Co Ltd
China Mobile Communications Ltd Research Institute
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • 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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The embodiment of the invention provides a method, a device and a computer readable storage medium for optimizing wireless resources, wherein the method comprises the following steps: the non-real-time wireless access network intelligent controller generates relevant information for configuring wireless resource optimization based on the cell characteristic parameters; and sending the related information to a near real-time wireless access network intelligent controller, and configuring a wireless resource optimization algorithm for the near real-time wireless access network intelligent controller, and finally completing wireless resource optimization operation by a base station.

Description

Wireless resource optimization method, device and computer readable storage medium
Technical Field
The present invention relates to the field of mobile communications technologies, and in particular, to a method and an apparatus for optimizing radio resources, and a computer-readable storage medium.
Background
In order to meet the data traffic and various service requirements which are explosively increased nowadays, and provide more smooth and satisfactory services for users, more and more frequency band resources are deployed in a wireless mobile network. However, monitoring the KPI index of the current network shows that the problem of capacity difference between different carrier frequencies/cells is significant, and often one cell cannot provide normal service due to resource exhaustion, and another cell is idle and thus resources are wasted. In order to solve the problem of uneven service sharing, the wireless resource optimization of the network is required, and related optimization means include load balancing, dynamic spectrum sharing and the like.
In the existing network, the optimization effect of the radio resource optimization algorithm on the optimization variables is little under certain scenes, and the performance of the radio resource optimization algorithm is deteriorated due to the fact that the radio resource optimization algorithm cannot be flexibly adapted to the variable scenes.
Disclosure of Invention
In view of the above, embodiments of the present invention are directed to a method, an apparatus, and a computer-readable storage medium for optimizing radio resources.
In order to achieve the above purpose, the technical solution of the embodiment of the present invention is realized as follows:
the embodiment of the invention provides a wireless resource optimization method, which is applied to a non-real-time wireless access network intelligent controller and comprises the following steps:
generating related information for configuring wireless resource optimization based on the cell characteristic parameters;
and sending the related information to a near real-time wireless access network intelligent controller, and configuring a wireless resource optimization algorithm for the near real-time wireless access network intelligent controller, and finally completing wireless resource optimization operation by a base station.
Wherein the sending the related information to the near real-time radio access network intelligent controller comprises:
and sending the related information to the near-real-time wireless access network intelligent controller through an A1 interface.
Wherein the related information is:
the cell classification basis, or the cell classification result, or the parameters for customizing the configured radio resource optimization algorithm.
The cell classification basis is as follows: a cell classification criterion, and/or a cell classification model, and/or a cell clustering model.
When the related information is a cell classification basis, the generating of the related information for configuring the optimization of the radio resources includes:
configuring a cell classification standard; alternatively, the first and second electrodes may be,
and training to generate a cell classification model and/or a cell clustering model.
Wherein, the input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and adjacent cell transmitting power, cell and adjacent cell antenna parameters, cell and adjacent cell use frequency bands and cell and adjacent cell site positions;
the output of the cell classification model is one or more of the following: the cell coverage type and the pilot frequency include coverage cells, same-frequency overlapping coverage cells and pilot frequency overlapping coverage cells;
the input of the cell clustering model is one or more of the following: cell service characteristic parameters, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the output of the cell clustering model is one or more of the following: and clustering out different cell service categories.
When the relevant information is a cell classification result, the generating of the relevant information for configuring the optimization of the radio resource includes:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
and determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter.
Wherein the cell category includes, but is not limited to, a cell coverage category and/or a cell traffic category; wherein the content of the first and second substances,
the cell coverage categories include one or more of:
common-frequency common coverage, pilot-frequency containing coverage, common-frequency overlapping coverage and pilot-frequency overlapping coverage;
the cell traffic classes include: service class 1.. service class k, where k service classes are generated by a clustering algorithm, each class carries a corresponding service feature profile, and the profile includes each service type ID, each service type traffic ratio, each service type average data packet size, and each service type average data packet arrival rate.
When the related information is a parameter for customizing an infinite resource configuration optimization algorithm, the generating of the related information for configuring the wireless resource optimization includes:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter;
and determining a radio resource optimization algorithm parameter based on the category of the cell.
Wherein the radio resource optimization algorithm parameters include one or more of:
triggering a monitoring index or a combined monitoring index of a radio resource optimization algorithm and the weight of each index;
a monitoring indicator or a combined monitoring indicator triggering threshold;
optimizing a target by an algorithm;
and (5) an algorithm termination condition.
Optionally, the method further includes:
and acquiring the cell characteristic parameters.
Wherein the cell characteristic parameters include: base station side cell characteristic parameters and network management side cell characteristic parameters; wherein the content of the first and second substances,
the base station side cell characteristic parameters include but are not limited to one or more of the following:
service type identification ID, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the network management side cell characteristic parameters include but are not limited to one or more of the following:
the method comprises the following steps of a neighbor cell list of a cell, cell and neighbor cell transmitting power, cell and neighbor cell antenna parameters, cell and neighbor cell use frequency bands and cell and neighbor cell site positions.
The embodiment of the invention also provides a wireless resource optimization method, which is applied to the near real-time wireless access network intelligent controller and comprises the following steps:
receiving related information for configuring radio resource optimization;
and configuring a radio resource optimization algorithm based on the related information, and finally completing radio resource optimization operation by the base station.
Wherein the receiving related information for configuring radio resource optimization comprises:
and receiving the related information sent by the non-real-time radio access network intelligent controller through an A1 interface.
Wherein the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing and configuring the infinite resource optimization algorithm.
When the relevant information is a cell classification basis, the configuring a radio resource optimization algorithm based on the relevant information includes:
determining the category of the cell based on the cell classification basis, the base station side cell characteristic parameters acquired in real time through the E2 interface and the network management side cell characteristic parameters received through the A1 interface;
generating a radio resource optimization algorithm parameter based on the category of the cell;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
When the relevant information is a cell classification result, the configuring a radio resource optimization algorithm based on the relevant information includes:
generating a radio resource optimization algorithm parameter based on the received cell belonging category;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
When the related information is a parameter for customizing an infinite resource optimization algorithm, the configuring a radio resource optimization algorithm based on the related information includes:
and configuring a radio resource optimization algorithm based on the received radio resource optimization algorithm parameters.
The embodiment of the invention also provides a wireless resource optimization device, which is applied to the intelligent controller of the non-real-time wireless access network and comprises the following steps:
a generation module, configured to generate relevant information for configuring radio resource optimization based on the cell characteristic parameter;
and the sending module is used for sending the related information to the near-real-time wireless access network intelligent controller, configuring a wireless resource optimization algorithm for the near-real-time wireless access network intelligent controller, and finally completing wireless resource optimization operation by the base station.
The embodiment of the invention also provides a wireless resource optimization device, which is applied to the near real-time wireless access network intelligent controller and comprises the following components:
the receiving module is used for receiving related information for configuring wireless resource optimization;
and the configuration module is used for configuring a wireless resource optimization algorithm based on the related information and finally completing wireless resource optimization operation by the base station.
The embodiment of the invention also provides a wireless resource optimization device, which comprises: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is configured to perform the steps of the above method when running the computer program.
Embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-mentioned method.
According to the wireless resource optimization method, the wireless resource optimization device and the computer-readable storage medium provided by the embodiment of the invention, the non-real-time wireless access network intelligent controller generates relevant information for configuring wireless resource optimization based on the cell characteristic parameters; and sending the related information to a near real-time wireless access network intelligent controller, and configuring a wireless resource optimization algorithm for the near real-time wireless access network intelligent controller, and finally completing wireless resource optimization operation by a base station. The embodiment of the invention aims at the algorithm parameter configuration for customizing the cells in the related information, more finely adapts to different scenes, more accurately triggers the wireless resource optimization algorithm, and can overcome the defect that the traditional network management configuration method adopts a uniform standard to configure the same algorithm parameters for all the cells without distinguishing so as to deteriorate the wireless resource optimization performance.
Drawings
Fig. 1 is a first flowchart illustrating a radio resource optimization method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a radio resource optimization method according to a second embodiment of the present invention;
fig. 3 is a first schematic structural diagram of a radio resource optimization apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a radio resource optimization apparatus according to a second embodiment of the present invention;
fig. 5 is a schematic diagram of a radio resource optimization method based on an O-RAN architecture according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a radio resource optimization method based on O-RAN architecture according to a second embodiment of the present invention;
fig. 7 is a schematic diagram of a radio resource optimization method based on an O-RAN architecture according to a third embodiment of the present disclosure.
Detailed Description
The invention is described below with reference to the figures and examples.
An embodiment of the present invention provides a method for optimizing radio resources, as shown in fig. 1, where the method is applied to a non-real-time radio access network intelligent controller, and includes:
step 101: generating related information for configuring wireless resource optimization based on the cell characteristic parameters;
step 102: and sending the related information to a near real-time wireless access network intelligent controller, and configuring a wireless resource optimization algorithm for the near real-time wireless access network intelligent controller, and finally completing wireless resource optimization operation by a base station.
The embodiment of the invention aims at the algorithm parameter configuration for customizing the cells in the related information, more finely adapts to different scenes, more accurately triggers the wireless resource optimization algorithm, and can overcome the defect that the traditional network management configuration method adopts a uniform standard to configure the same algorithm parameters for all the cells without distinguishing so as to deteriorate the wireless resource optimization performance.
In this embodiment of the present invention, the sending the related information to the near real-time radio access network intelligent controller includes:
and sending the related information to the near-real-time wireless access network intelligent controller through an A1 interface.
In the embodiment of the present invention, the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing the configured radio resource optimization algorithm.
In the embodiment of the present invention, the cell classification basis is: a cell classification criterion, and/or a cell classification model, and/or a cell clustering model.
In an embodiment of the present invention, when the relevant information is a cell classification basis, the generating the relevant information for configuring radio resource optimization includes:
configuring a cell classification standard; alternatively, the first and second electrodes may be,
and training to generate a cell classification model and/or a cell clustering model.
In the embodiment of the present invention, the first and second substrates,
the input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and adjacent cell transmitting power, cell and adjacent cell antenna parameters, cell and adjacent cell use frequency bands and cell and adjacent cell site positions;
the output of the cell classification model is one or more of the following: the cell coverage type and the pilot frequency include coverage cells, same-frequency overlapping coverage cells and pilot frequency overlapping coverage cells;
the input of the cell clustering model is one or more of the following: cell service characteristic parameters, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the output of the cell clustering model is one or more of the following: and clustering different cell service classes.
In an embodiment of the present invention, when the related information is a cell classification result, the generating related information for configuring radio resource optimization includes:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
and determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter.
In the embodiment of the present invention, the cell category includes, but is not limited to, a cell coverage category and/or a cell service category; wherein the content of the first and second substances,
the cell coverage categories include one or more of:
common-frequency common coverage, pilot-frequency containing coverage, common-frequency overlapping coverage and pilot-frequency overlapping coverage;
the cell traffic classes include: service class 1.. service class k, where k service classes are generated by a clustering algorithm, each class carries a corresponding service feature profile, and the profile includes each service type ID, each service type traffic ratio, each service type average data packet size, and each service type average data packet arrival rate.
In an embodiment of the present invention, when the related information is a parameter for customizing an infinite resource optimization algorithm, the generating the related information for configuring radio resource optimization includes:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter;
and determining a radio resource optimization algorithm parameter based on the category of the cell.
In an embodiment of the present invention, the radio resource optimization algorithm parameters include one or more of the following:
triggering a monitoring index or a combined monitoring index of a radio resource optimization algorithm and the weight of each index;
a monitoring indicator or a combined monitoring indicator triggering threshold;
optimizing a target by an algorithm;
and (5) an algorithm termination condition.
In one embodiment of the present invention, the method further comprises:
and acquiring the cell characteristic parameters.
In the embodiment of the present invention, the cell characteristic parameters include: base station side cell characteristic parameters and network management side cell characteristic parameters; wherein, the first and the second end of the pipe are connected with each other,
the base station side cell characteristic parameters include, but are not limited to, one or more of the following:
service type identification ID, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the network management side cell characteristic parameters include but are not limited to one or more of the following:
the method comprises the following steps of a neighbor cell list of a cell, cell and neighbor cell transmitting power, cell and neighbor cell antenna parameters, cell and neighbor cell use frequency bands and cell and neighbor cell site positions.
An embodiment of the present invention further provides a method for optimizing radio resources, as shown in fig. 2, where the method is applied to an intelligent controller of a near real-time radio access network, and includes:
step 201: receiving related information for configuring radio resource optimization;
step 202: and configuring a radio resource optimization algorithm based on the related information, and finally completing radio resource optimization operation by the base station.
In this embodiment of the present invention, the receiving related information for configuring radio resource optimization includes:
and receiving the related information sent by the intelligent controller of the non-real-time radio access network through an A1 interface.
In the embodiment of the present invention, the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing and configuring the infinite resource optimization algorithm.
In an embodiment of the present invention, when the related information is a cell classification basis, the configuring a radio resource optimization algorithm based on the related information includes:
determining the category of the cell based on the cell classification basis, the cell characteristic parameters of the base station side acquired in real time by the E2 interface and the cell characteristic parameters of the network management side received by the A1 interface;
generating a radio resource optimization algorithm parameter based on the category to which the cell belongs;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
In an embodiment of the present invention, when the relevant information is a cell classification result, the configuring a radio resource optimization algorithm based on the relevant information includes:
generating a radio resource optimization algorithm parameter based on the category of the received cell;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
In another embodiment of the present invention, when the related information is a parameter for customizing an infinite resource optimization algorithm, the configuring a radio resource optimization algorithm based on the related information includes:
and configuring a radio resource optimization algorithm based on the received radio resource optimization algorithm parameters.
In order to implement the foregoing method embodiment, an embodiment of the present invention further provides a radio resource optimization apparatus, as shown in fig. 3, where the apparatus is applied to a non-real-time radio access network intelligent controller, and the apparatus includes:
a generating module 301, configured to generate relevant information for configuring radio resource optimization based on the cell characteristic parameter;
a sending module 302, configured to send the relevant information to the near-real-time radio access network intelligent controller, where the near-real-time radio access network intelligent controller configures a radio resource optimization algorithm, and a base station finally completes radio resource optimization operation.
In this embodiment of the present invention, the sending module 302 sending the relevant information to the near real-time radio access network intelligent controller includes:
and sending the related information to the near-real-time wireless access network intelligent controller through an A1 interface.
In the embodiment of the present invention, the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing the configured radio resource optimization algorithm.
In the embodiment of the present invention, the cell classification basis is: a cell classification criterion, and/or a cell classification model, and/or a cell clustering model.
In an embodiment of the present invention, when the related information is a cell classification basis, the generating module 301 generates related information for configuring radio resource optimization, including:
configuring a cell classification standard; alternatively, the first and second electrodes may be,
and training to generate a cell classification model and/or a cell clustering model.
In the embodiment of the present invention, the first and second substrates,
the input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and adjacent cell transmitting power, cell and adjacent cell antenna parameters, cell and adjacent cell use frequency bands and cell and adjacent cell site positions;
the output of the cell classification model is one or more of the following: the cell coverage type and the pilot frequency include coverage cells, same-frequency overlapping coverage cells and pilot frequency overlapping coverage cells;
the input of the cell clustering model is one or more of the following: cell service characteristic parameters, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the output of the cell clustering model is one or more of the following: and clustering out different cell service categories.
In an embodiment of the present invention, when the relevant information is a cell classification result, the generating module 301 generates relevant information for configuring radio resource optimization, including:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
and determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter.
In the embodiment of the present invention, the cell category includes, but is not limited to, a cell coverage category and/or a cell service category; wherein the content of the first and second substances,
the cell coverage categories include one or more of:
common-frequency common coverage, pilot-frequency containing coverage, common-frequency overlapping coverage and pilot-frequency overlapping coverage;
the cell traffic classes include: service class 1.. service class k, where k service classes are generated by a clustering algorithm, each class carries a corresponding service feature profile, and the profile includes each service type ID, each service type traffic ratio, each service type average data packet size, and each service type average data packet arrival rate.
In an embodiment of the present invention, when the related information is a parameter for customizing an infinite resource configuration optimization algorithm, the generating module 301 generates the related information for configuring the radio resource optimization, including:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter;
and determining a radio resource optimization algorithm parameter based on the category of the cell.
In an embodiment of the present invention, the radio resource optimization algorithm parameters include one or more of the following:
triggering a monitoring index or a combined monitoring index of a radio resource optimization algorithm and the weight of each index;
a monitoring indicator or a combined monitoring indicator triggering threshold;
optimizing a target by an algorithm;
and (5) an algorithm termination condition.
In an embodiment of the present invention, the generating module 301 is further configured to obtain the cell characteristic parameter.
In the embodiment of the present invention, the cell characteristic parameters include: base station side cell characteristic parameters and network management side cell characteristic parameters; wherein the content of the first and second substances,
the base station side cell characteristic parameters include but are not limited to one or more of the following:
service type identification ID, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the network management side cell characteristic parameters include but are not limited to one or more of the following:
the method comprises the following steps of a neighbor cell list of a cell, cell and neighbor cell transmitting power, cell and neighbor cell antenna parameters, cell and neighbor cell use frequency bands and cell and neighbor cell site positions.
An embodiment of the present invention further provides a radio resource optimization apparatus, as shown in fig. 4, where the apparatus is applied to a near real-time radio access network intelligent controller, and includes:
a receiving module 401, configured to receive related information for configuring radio resource optimization;
a configuration module 402, configured to configure a radio resource optimization algorithm based on the relevant information, and the base station finally completes the radio resource optimization operation.
In this embodiment of the present invention, the receiving module 401 receives relevant information for configuring radio resource optimization, including:
and receiving the related information sent by the intelligent controller of the non-real-time radio access network through an A1 interface.
In the embodiment of the present invention, the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing and configuring the infinite resource optimization algorithm.
In an embodiment of the present invention, when the relevant information is a cell classification basis, the configuring module 402 configures a radio resource optimization algorithm based on the relevant information, including:
determining the category of the cell based on the cell classification basis, the cell characteristic parameters of the base station side acquired in real time by the E2 interface and the cell characteristic parameters of the network management side received by the A1 interface;
generating a radio resource optimization algorithm parameter based on the category to which the cell belongs;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
In an embodiment of the present invention, when the relevant information is a cell classification result, the configuring module 402 configures a radio resource optimization algorithm based on the relevant information, including:
generating a radio resource optimization algorithm parameter based on the received cell belonging category;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
In another embodiment of the present invention, when the related information is a parameter for customizing an infinite resource optimization algorithm, the configuring module 402 configures a radio resource optimization algorithm based on the related information, including:
and configuring a radio resource optimization algorithm based on the received radio resource optimization algorithm parameters.
The embodiment of the invention also provides a wireless resource optimization device, which is applied to the intelligent controller of the non-real-time wireless access network and comprises the following steps: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is configured to execute, when running the computer program:
generating related information for configuring wireless resource optimization based on the cell characteristic parameters;
and sending the related information to a near real-time wireless access network intelligent controller, and configuring a wireless resource optimization algorithm for the near real-time wireless access network intelligent controller, and finally completing wireless resource optimization operation by a base station.
When the relevant information is sent to the near real-time wireless access network intelligent controller, the processor is further configured to execute, when the computer program is run:
and sending the related information to the near-real-time wireless access network intelligent controller through an A1 interface.
Wherein the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing the configured radio resource optimization algorithm.
The cell classification basis is as follows: a cell classification criterion, and/or a cell classification model, and/or a cell clustering model.
When the relevant information is a cell classification basis, and when the relevant information for configuring the radio resource optimization is generated, the processor is further configured to execute, when the computer program is run, the following steps:
configuring a cell classification standard; alternatively, the first and second electrodes may be,
and training to generate a cell classification model and/or a cell clustering model.
Wherein, the input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and adjacent cell transmitting power, cell and adjacent cell antenna parameters, cell and adjacent cell use frequency bands and cell and adjacent cell site positions;
the output of the cell classification model is one or more of the following: the cell coverage type and the pilot frequency include coverage cells, same-frequency overlapping coverage cells and pilot frequency overlapping coverage cells;
the cell clustering model has the following input(s): cell service characteristic parameters, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the output of the cell clustering model is one or more of the following: and clustering different cell service classes.
When the relevant information is a cell classification result, when the relevant information for configuring the radio resource optimization is generated, and the processor is further configured to execute, when the computer program is run, the following steps:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
and determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter.
Wherein the cell category includes, but is not limited to, a cell coverage category and/or a cell traffic category; wherein the content of the first and second substances,
the cell coverage categories include one or more of:
common-frequency common coverage, pilot-frequency containing coverage, common-frequency overlapping coverage and pilot-frequency overlapping coverage;
the cell traffic classes include: service class 1.. service class k, where k service classes are generated by a clustering algorithm, each class carries a corresponding service feature profile, and the profile includes each service type ID, each service type traffic ratio, each service type average data packet size, and each service type average data packet arrival rate.
When the related information is a parameter for customizing an infinite resource configuration optimization algorithm, and when the related information for configuring the wireless resource optimization is generated, the processor is further configured to execute, when the computer program is run, the following steps:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter;
and determining a radio resource optimization algorithm parameter based on the category of the cell.
Wherein the radio resource optimization algorithm parameters include one or more of:
triggering a monitoring index or a combined monitoring index of a radio resource optimization algorithm and the weight of each index;
a monitoring indicator or a combined monitoring indicator triggering threshold;
optimizing a target by an algorithm;
and (5) an algorithm termination condition.
The processor is further configured to, when executing the computer program, perform:
and acquiring the cell characteristic parameters.
Wherein the cell characteristic parameters include: base station side cell characteristic parameters and network management side cell characteristic parameters; wherein the content of the first and second substances,
the base station side cell characteristic parameters include but are not limited to one or more of the following:
service type identification ID, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the network management side cell characteristic parameters include but are not limited to one or more of the following:
the method comprises the following steps of a neighbor cell list of a cell, cell and neighbor cell transmitting power, cell and neighbor cell antenna parameters, cell and neighbor cell use frequency bands and cell and neighbor cell site positions.
The embodiment of the invention also provides a wireless resource optimization device, which is applied to the near real-time wireless access network intelligent controller and comprises the following components: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is configured to execute, when running the computer program:
receiving related information for configuring radio resource optimization;
and configuring a radio resource optimization algorithm based on the related information, and finally completing radio resource optimization operation by the base station.
The processor, when receiving the relevant information for configuring the radio resource optimization, is further configured to execute, when running the computer program:
and receiving the related information sent by the intelligent controller of the non-real-time radio access network through an A1 interface.
Wherein the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing and configuring the infinite resource optimization algorithm.
When the relevant information is a cell classification basis, and when a radio resource optimization algorithm is configured based on the relevant information, the processor is further configured to execute, when the computer program is run:
determining the category of the cell based on the cell classification basis, the cell characteristic parameters of the base station side acquired in real time by the E2 interface and the cell characteristic parameters of the network management side received by the A1 interface;
generating a radio resource optimization algorithm parameter based on the category to which the cell belongs;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
When the relevant information is a cell classification result, and when a radio resource optimization algorithm is configured based on the relevant information, the processor is further configured to execute, when the computer program is run, the following steps:
generating a radio resource optimization algorithm parameter based on the received cell belonging category;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
When the related information is a parameter for customizing a wireless resource optimization algorithm, and when the wireless resource optimization algorithm is configured based on the related information, the processor is further configured to execute, when the computer program is run:
and configuring a radio resource optimization algorithm based on the received radio resource optimization algorithm parameters.
It should be noted that: the apparatus provided in the foregoing embodiment is only illustrated by the division of the program modules when performing the radio resource optimization, and in practical applications, the processing allocation may be completed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the apparatus provided in the above embodiments and the corresponding method embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments and are not described herein again.
In an exemplary embodiment, the embodiment of the present invention also provides a computer-readable storage medium, which may be a Memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disc, or CD-ROM; or may be a variety of devices including one or any combination of the above memories, such as a mobile phone, computer, tablet device, personal digital assistant, etc.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs:
generating related information for configuring wireless resource optimization based on the cell characteristic parameters;
and sending the related information to a near real-time wireless access network intelligent controller, and configuring a wireless resource optimization algorithm for the near real-time wireless access network intelligent controller, and finally completing wireless resource optimization operation by a base station.
When the relevant information is sent to the near real-time radio access network intelligent controller, and the computer program is executed by the processor, the method further executes:
and sending the related information to the near-real-time wireless access network intelligent controller through an A1 interface.
Wherein the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing the configured radio resource optimization algorithm.
The cell classification basis is as follows: a cell classification criterion, and/or a cell classification model, and/or a cell clustering model.
When the relevant information is a cell classification basis, and when the relevant information for configuring radio resource optimization is generated, and the computer program is executed by the processor, the method further performs:
configuring a cell classification standard; alternatively, the first and second electrodes may be,
and training to generate a cell classification model and/or a cell clustering model.
Wherein, the input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and adjacent cell transmitting power, cell and adjacent cell antenna parameters, cell and adjacent cell use frequency bands and cell and adjacent cell site positions;
the output of the cell classification model is one or more of the following: the cell coverage type, the pilot frequency coverage cell, the same-frequency overlapping coverage cell and the pilot frequency overlapping coverage cell;
the input of the cell clustering model is one or more of the following: cell service characteristic parameters, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the output of the cell clustering model is one or more of the following: and clustering different cell service classes.
When the relevant information is a cell classification result, and when the relevant information for configuring the radio resource optimization is generated, the computer program is executed by a processor, and further executes:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
and determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter.
Wherein the cell category includes, but is not limited to, a cell coverage category and/or a cell traffic category; wherein the content of the first and second substances,
the cell coverage categories include one or more of:
common-frequency common coverage, pilot-frequency containing coverage, common-frequency overlapping coverage and pilot-frequency overlapping coverage;
the cell traffic classes include: service class 1.. service class k, where k service classes are generated by a clustering algorithm, each class carries a corresponding service feature profile, and the profile includes each service type ID, the ratio of service amount of each service type, the average data packet size of each service type, and the average data packet arrival rate of each service type.
When the related information is a parameter for customizing a configured infinite resource optimization algorithm, and when the related information for configuring wireless resource optimization is generated, the computer program is executed by the processor, further executing:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter;
and determining a radio resource optimization algorithm parameter based on the category of the cell.
Wherein the radio resource optimization algorithm parameters include one or more of:
triggering a monitoring index or a combined monitoring index of a radio resource optimization algorithm and the weight of each index;
a monitoring indicator or a combined monitoring indicator triggering threshold;
optimizing a target by an algorithm;
and (5) an algorithm termination condition.
The computer program, when executed by the processor, further performs:
and acquiring the cell characteristic parameters.
Wherein the cell characteristic parameters include: base station side cell characteristic parameters and network management side cell characteristic parameters; wherein the content of the first and second substances,
the base station side cell characteristic parameters include but are not limited to one or more of the following:
service type identification ID, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the network management side cell characteristic parameters include but are not limited to one or more of the following:
the method comprises the following steps of a neighbor cell list of a cell, cell and neighbor cell transmitting power, cell and neighbor cell antenna parameters, cell and neighbor cell use frequency bands and cell and neighbor cell site positions.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs:
receiving related information for configuring radio resource optimization;
and configuring a radio resource optimization algorithm based on the related information, and finally completing radio resource optimization operation by the base station.
When the receiving is performed for configuring the related information for the optimization of the wireless resources, the computer program is executed by the processor, and further performs:
and receiving the related information sent by the intelligent controller of the non-real-time radio access network through an A1 interface.
Wherein the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing and configuring the infinite resource optimization algorithm.
When the relevant information is a cell classification basis, and when the radio resource optimization algorithm is configured based on the relevant information, the computer program is executed by a processor, and further executes:
determining the category of the cell based on the cell classification basis, the cell characteristic parameters of the base station side acquired in real time by the E2 interface and the cell characteristic parameters of the network management side received by the A1 interface;
generating a radio resource optimization algorithm parameter based on the category to which the cell belongs;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
When the relevant information is a cell classification result, and when the radio resource optimization algorithm is configured based on the relevant information, the computer program is executed by a processor, and further executes:
generating a radio resource optimization algorithm parameter based on the received cell belonging category;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
When the related information is a parameter for customizing a wireless resource optimization algorithm, and when the wireless resource optimization algorithm is configured based on the related information, the computer program is executed by the processor, further executing:
and configuring a radio resource optimization algorithm based on the received radio resource optimization algorithm parameters.
The invention is described below in conjunction with the scenario embodiments.
With the wireless network coverage scenario and the distribution of the provided service types and service types becoming more and more diverse, the cell characteristics presented between the cells are often different, for example, most of the accessed services of the cell a are the eMBB services, most of the accessed services of the cell B are the IoT services, and the service characteristics of the two services, such as the packet size and the packet arrival time interval, are different, so the cell service characteristics presented by the cell a and the cell B are also different. Accordingly, the radio resource optimization algorithm parameters of different cells should be configured in a customized manner according to different cell characteristics, for example, the service packet of cell a is larger than that of cell B, and the arrival time interval of cell B is shorter, so for the parameters of the cell load balancing algorithm (a radio resource optimization algorithm), such as the cell load monitoring period, the cell a load monitoring period should be set shorter than that of cell B.
Therefore, the cells need to be classified more finely according to the characteristic parameters of the cells, and then the cell radio resource optimization algorithm parameters are generated and matched in a customized manner based on the cell categories.
Specifically, the embodiment proposes a radio resource optimization method based on a cell category based on an open radio access network (O-RAN) two-level intelligent controller (Non-real-time RAN intelligent controller, Near-real-time radio access network intelligent controller Near-RT RIC). Depending on the different distribution of functions between the two levels of RIC, three corresponding solutions are given by the following three embodiments.
The first embodiment is as follows:
this embodiment issues a cell classification result for an a1 interface, as shown in fig. 5, including:
the method comprises the following steps: the network manager/Non-RT RIC collects cell characteristic parameters of a base station side through an O1 interface, such as service type ID, service quantity of each service type, arrival time interval of data packets of each service type, size of data packets of each service type, arrival rate of data packets of each service type and the like;
step two: combining the historical cell characteristic parameters of the base station side and the network management side, the network management/Non-RT RIC formulates a cell classification standard, or trains a cell classification and/or cell clustering model, determines the type of a cell according to the current cell characteristic parameters, and issues the cell type to Near-RT RIC through an A1 interface. Wherein, the cell characteristic parameter of the network management side includes: a neighbor cell list of a cell, cell and neighbor cell transmitting power, cell and neighbor cell antenna parameters, cell and neighbor cell use frequency bands, cell and neighbor cell site positions and the like; the cell categories issued by the a1 interface (i.e. the categories to which the cells belong) include, but are not limited to: a cell coverage class and/or a cell traffic class, wherein:
the cell coverage categories include: common-frequency common coverage, pilot-frequency containing coverage, common-frequency overlapping coverage, pilot-frequency overlapping coverage and the like;
the cell service classes include: service class 1.. service class k, where k service classes are generated by a clustering algorithm, each class carries a corresponding profile, and the profile may include each service type ID, each service type traffic proportion, each service type average data packet size, and each service type average data packet arrival rate;
step three: the Near-RT RIC generates a wireless resource optimization algorithm parameter and configures a wireless resource optimization algorithm according to the received cell type;
step four: the configured radio resource optimization algorithm generates intelligent radio resource optimization control according to the cell-level/user-level real-time measurement parameters reported by the E2 interface and sends the intelligent radio resource optimization control to the base station, and the base station finally completes radio resource optimization operation.
Example two:
this embodiment issues a cell classification model/cell clustering model/cell classification standard for an a1 interface, as shown in fig. 6, including:
the method comprises the following steps: according to the collected cell characteristic parameters (same as the first embodiment), the webmaster/Non-RT RIC formulates a cell classification standard, or trains a cell classification and/or a cell clustering model, and issues the cell classification model and/or the cell clustering model and/or the cell classification standard and the webmaster side cell characteristic parameters to the Near-RT RIC through an A1 interface, so that the Near-RT RIC completes the cell classification with stronger real-time performance; wherein the content of the first and second substances,
for the cell classification model:
the input is as follows: cell coverage characteristic parameters such as a cell neighbor list, cell and neighbor transmitting power, cell and neighbor antenna parameters, cell and neighbor use frequency bands, cell and neighbor site positions and the like;
the output is: the cell coverage types output by the cell classification model are common coverage cells of the same frequency, different frequency coverage cells, overlapping coverage cells of the same frequency, overlapping coverage cells of the different frequency, and the like.
For the cell clustering model:
the input is as follows: cell service characteristic parameters such as service type ID, service amount of each service type, arrival time interval of data packets of each service type, size of data packets of each service type, arrival rate of data packets of each service type and other multidimensional service characteristics;
the output is: different cell service classes, such as service class 1, service class 2, service class K, clustered by a cell clustering model (such as K-means), wherein the profile corresponding to different profiles of each class may contain each service type ID, traffic proportion of each service type, average data packet size of each service type, and average data packet arrival rate of each service type.
The cell classification standard includes but is not limited to classifying into a large cell, a medium cell, a small cell and the like according to the data characteristics of the size of the cell service data packet;
step two: the Near-RT RIC acquires the characteristic parameters of the base station side cells in real time through an E2 interface according to the received cell classification/cell clustering model/cell classification standard, receives the characteristic parameters of the network management side cells through an A1 interface, and determines the cell types based on the acquired real-time cell characteristic parameters;
step three: and generating a radio resource optimization algorithm parameter based on the cell type, configuring a radio resource optimization algorithm, generating intelligent radio resource optimization control according to the cell level/user level real-time measurement parameter reported by the E2 interface by the configured radio resource optimization algorithm, and issuing the intelligent radio resource optimization control to the base station, wherein the base station finally completes the radio resource optimization operation.
Example three:
the embodiment issues configuration parameters of a cell radio resource optimization algorithm for an a1 interface, as shown in fig. 7, the configuration parameters include:
the method comprises the following steps: according to the collected cell characteristic parameters (same as the first embodiment), the webmaster/Non-RT RIC formulates a cell classification standard, or trains a cell classification and/or a cell clustering model, determines the category of the cell according to the real-time cell characteristic parameters, determines radio resource optimization algorithm parameters based on the cell category, and issues the radio resource optimization algorithm parameters to the Near-RT RIC through an a1 interface, wherein the radio resource optimization algorithm parameters issued through an a1 interface include:
1) triggering the monitoring index/combined monitoring index of the wireless resource optimization algorithm and the weight of each index:
for example: PRB utilization rate, PDCCH CCE utilization rate, RRC connection rate, user number and the like for data transmission; different cell service classes also present different characteristics to the occupation situation of control channel resources and data channel resources in the cell, so that the triggering conditions for triggering different cell resource optimization algorithms should be dynamically configured;
besides the above single monitoring index, the combined monitoring index and the weight of each index (for example, 60% of PRB utilization rate for data transmission + 40% of PDCCH CCE utilization rate) may also be configured, and the combined monitoring index may effectively avoid the algorithm trigger accuracy of the single index, and better meet the evaluation of the cell load in different service scenarios (for example, ultra wideband service and wide connection service).
2) Monitoring index/combined monitoring index trigger threshold:
for example: threshold value of total occupancy rate of PRB: 70 percent; user number threshold value: 200 of a carrier; 60% PRB utilization for data transmission + 40% PDCCH CCE utilization combined threshold: 70 percent;
3) the algorithm optimizes the target:
for example: minimizing load variance of the serving cell and the neighboring cell, minimizing user handover times, minimizing user rate loss, achieving user guaranteed rate, and a combined optimization objective (e.g., min (serving cell and neighboring cell load variance + user handover times + user rate loss)) based on the above-mentioned single optimization objectives.
4) Algorithm termination conditions are as follows:
for example: the user experience rate reaches a specified guarantee value, or the cell load drops to a specified threshold, and the like.
Step two: the Near-RT RIC configures a radio resource optimization algorithm according to the received cell radio resource optimization algorithm parameters, the configured radio resource optimization algorithm generates intelligent radio resource optimization control according to cell level/user level real-time measurement parameters reported by an E2 interface and sends the intelligent radio resource optimization control to the base station, and the base station finally completes radio resource optimization operation.
Example four
The embodiment provides a specific application scenario, which is specifically as follows, and includes:
the method comprises the following steps: Non-RT RIC collects cell characteristic parameters of a base station side through an O1 interface, wherein the cell characteristic parameters comprise:
each service type ID;
traffic volume of each service type;
the arrival time interval of each service type data packet;
the size of each service type data packet;
packet arrival rate for each traffic type.
Step two: based on the collected cell service characteristic parameters, the Non-RT RIC trains a cell clustering model by using a Kmeans unsupervised learning model to cluster the service classes of the cells to obtain k classes, and each class generates a corresponding profile (cell service class 1+ profile 1, cell service class 2+ profile 2.. cell service class k + profile k). The profile includes:
the traffic type IDs that belong to this category (ID 1: video, ID 2: voice, ID 3: IoT traffic);
the percentage of each traffic type traffic (ID 1: 70%, ID 2: 20%, ID 3: 10%);
average packet size for each traffic type (ID 1: 1000 bytes, ID 2: 300 bytes, ID 3: 100 bits);
average packet arrival rate for each traffic type (ID 1: 5 packets/sec, ID 2: 2 packets/sec, ID 3: 15 packets/sec).
Step three: through the clustering model for classifying the service classes of the cells, the Non-RT RIC outputs the class to which the current cell belongs and the corresponding profile according to the service characteristic parameters of the current cell, and the Non-RT RIC analyzes the profile of the class to which the current cell belongs to generate wireless resource optimization algorithm parameters, wherein the wireless resource optimization algorithm parameters comprise:
the triggering load balancing algorithm is a combined index: PRB utilization + PDCCH CCE utilization for data transmission;
combined index weight: 60 percent and 40 percent;
combined index trigger threshold: 70 percent;
the algorithm optimizes the target: the user guarantee rate is achieved;
algorithm termination conditions are as follows: the average experience rate of the user reaches 1000 bit/s.
Step four: the generated wireless resource optimization algorithm parameters are issued to Near-RT RIC through an A1 interface and are used for configuring a load balancing algorithm;
step five: and the load balancing algorithm operates according to the configured wireless resource optimization algorithm parameters to complete the wireless resource optimization operation.
The embodiment of the invention generates relevant information for configuring wireless resource optimization based on the cell characteristic parameters; and configuring a radio resource optimization algorithm based on the related information, performing customized algorithm parameter configuration on the cells according to cell classification results in the related information, more finely adapting to different scenes, more accurately triggering the radio resource optimization algorithm, and overcoming the defect that the traditional network management configuration method adopts a uniform standard to configure the same algorithm parameters for all the cells without distinction, so that the radio resource optimization performance is poor.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.

Claims (22)

1. A wireless resource optimization method is applied to a non-real-time wireless access network intelligent controller and comprises the following steps:
generating related information for configuring wireless resource optimization based on the cell characteristic parameters;
and sending the related information to a near real-time wireless access network intelligent controller, and configuring a wireless resource optimization algorithm for the near real-time wireless access network intelligent controller, and finally completing wireless resource optimization operation by a base station.
2. The method of claim 1, wherein sending the relevant information to a near real-time radio access network intelligent controller comprises:
and sending the related information to the near-real-time wireless access network intelligent controller through an A1 interface.
3. The method of claim 1, wherein the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing the configured radio resource optimization algorithm.
4. The method of claim 3, wherein the cell classification is based on: a cell classification criterion, and/or a cell classification model, and/or a cell clustering model.
5. The method of claim 4, wherein when the related information is a cell classification basis, the generating related information for configuring radio resource optimization comprises:
configuring a cell classification standard; alternatively, the first and second electrodes may be,
and training to generate a cell classification model and/or a cell clustering model.
6. The method of claim 5,
the input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and adjacent cell transmitting power, cell and adjacent cell antenna parameters, cell and adjacent cell use frequency bands and cell and adjacent cell site positions;
the output of the cell classification model is one or more of the following: the cell coverage type and the pilot frequency include coverage cells, same-frequency overlapping coverage cells and pilot frequency overlapping coverage cells;
the cell clustering model has the following input(s): cell service characteristic parameters, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the output of the cell clustering model is one or more of the following: and clustering different cell service classes.
7. The method of claim 4, wherein when the related information is a cell classification result, the generating related information for configuring radio resource optimization comprises:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
and determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter.
8. The method of claim 7, wherein the cell category includes but is not limited to a cell coverage category and/or a cell traffic category; wherein the content of the first and second substances,
the cell coverage categories include one or more of:
common-frequency common coverage, pilot-frequency containing coverage, common-frequency overlapping coverage and pilot-frequency overlapping coverage;
the cell traffic classes include: service class 1.. service class k, where k service classes are generated by a clustering algorithm, each class carries a corresponding service feature profile, and the profile includes each service type ID, each service type traffic ratio, each service type average data packet size, and each service type average data packet arrival rate.
9. The method of claim 4, wherein when the related information is a parameter for customizing a configuration infinite resource optimization algorithm, the generating related information for configuring radio resource optimization comprises:
configuring a cell classification standard or training to generate a cell classification model and/or a cell clustering model;
determining the category of the cell based on the cell classification standard and/or the cell classification model and/or the cell clustering model and the current cell characteristic parameter;
and determining a radio resource optimization algorithm parameter based on the category of the cell.
10. The method of claim 9, wherein the radio resource optimization algorithm parameters include one or more of:
triggering a monitoring index or a combined monitoring index of a radio resource optimization algorithm and the weight of each index;
a monitoring indicator or a combined monitoring indicator triggering threshold;
optimizing a target by an algorithm;
and (5) an algorithm termination condition.
11. The method of claim 1, further comprising:
and acquiring the cell characteristic parameters.
12. The method according to claim 1 or 11, wherein the cell characteristic parameter comprises: base station side cell characteristic parameters and network management side cell characteristic parameters; wherein the content of the first and second substances,
the base station side cell characteristic parameters include but are not limited to one or more of the following:
service type identification ID, service volume of each service type, arrival time interval of data packets of each service type, size of data packets of each service type and arrival rate of data packets of each service type;
the network management side cell characteristic parameters include but are not limited to one or more of the following:
the method comprises the following steps of a neighbor cell list of a cell, cell and neighbor cell transmitting power, cell and neighbor cell antenna parameters, cell and neighbor cell use frequency bands and cell and neighbor cell site positions.
13. A wireless resource optimization method is applied to a near real-time wireless access network intelligent controller and comprises the following steps:
receiving related information for configuring radio resource optimization;
and configuring a radio resource optimization algorithm based on the related information, and finally completing radio resource optimization operation by the base station.
14. The method of claim 13, wherein the receiving related information for configuring radio resource optimization comprises:
and receiving the related information sent by the non-real-time radio access network intelligent controller through an A1 interface.
15. The method according to claim 13, wherein the related information is:
the cell classification basis, or the cell classification result, or the parameter for customizing and configuring the infinite resource optimization algorithm.
16. The method of claim 15, wherein when the related information is a cell classification basis, the configuring the rrc algorithm based on the related information comprises:
determining the category of the cell based on the cell classification basis, the cell characteristic parameters of the base station side acquired in real time by the E2 interface and the cell characteristic parameters of the network management side received by the A1 interface;
generating a radio resource optimization algorithm parameter based on the category of the cell;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
17. The method of claim 15, wherein when the related information is a cell classification result, the configuring a radio resource optimization algorithm based on the related information comprises:
generating a radio resource optimization algorithm parameter based on the received cell belonging category;
and configuring a radio resource optimization algorithm based on the radio resource optimization algorithm parameters.
18. The method of claim 15, wherein when the related information is a parameter for customizing a configuration of an infinite resource optimization algorithm, the configuring a radio resource optimization algorithm based on the related information comprises:
and configuring a radio resource optimization algorithm based on the received radio resource optimization algorithm parameters.
19. A radio resource optimization device is applied to a non-real-time radio access network intelligent controller, and comprises the following components:
a generating module, configured to generate relevant information for configuring radio resource optimization based on the cell characteristic parameter;
and the sending module is used for sending the related information to the near-real-time wireless access network intelligent controller, configuring a wireless resource optimization algorithm for the near-real-time wireless access network intelligent controller, and finally completing wireless resource optimization operation by the base station.
20. A wireless resource optimization device is applied to a near real-time wireless access network intelligent controller, and comprises the following components:
the receiving module is used for receiving related information for configuring wireless resource optimization;
and the configuration module is used for configuring a wireless resource optimization algorithm based on the relevant information, and the base station finally completes wireless resource optimization operation.
21. An apparatus for optimizing radio resources, the apparatus comprising: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is adapted to perform the steps of the method of any one of claims 1-12 or to perform the steps of the method of any one of claims 13-18 when running the computer program.
22. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 12 or carries out the steps of the method of any one of claims 13 to 18.
CN202011340530.2A 2020-11-25 2020-11-25 Wireless resource optimization method, device and computer readable storage medium Pending CN114554509A (en)

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