A wireless resource optimization method, device and computer-readable storage medium

Abstract

An embodiment of the present invention provides a wireless resource optimization method, device and computer-readable storage medium, the method comprising: a non-real-time wireless access network intelligent controller generates relevant information for configuring wireless resource optimization based on cell characteristic parameters; sending the relevant information to a near-real-time wireless access network intelligent controller, for the near-real-time wireless access network intelligent controller to configure a wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to a wireless resource optimization method, device and computer-readable storage medium. Background Art

[0002] In order to cope with the explosive growth of data traffic and diverse business needs, and to provide users with smoother and more satisfactory services, more and more frequency band resources are deployed in wireless mobile networks. However, by monitoring the KPI indicators of the existing network, it is found that the capacity difference between different carrier frequencies / cells is prominent. Often one cell cannot provide normal services due to resource exhaustion, while another cell wastes resources due to idleness. In order to solve the problem of uneven business sharing, it is necessary to optimize the wireless resources of the network. The relevant optimization methods include load balancing, dynamic spectrum sharing, etc.

[0003] It is found in the existing network that the wireless resource optimization algorithm has little effect on the optimization variables in some scenarios, and the performance of the wireless resource optimization algorithm deteriorates because it cannot flexibly adapt to changing scenarios. Summary of the invention

[0004] In view of this, embodiments of the present invention are intended to provide a wireless resource optimization method, device, and computer-readable storage medium.

[0005] To achieve the above object, the technical solution of the embodiment of the present invention is implemented as follows:

[0006] An embodiment of the present invention provides a wireless resource optimization method, which is applied to a non-real-time wireless access network intelligent controller, including:

[0007] Generate relevant information for configuring wireless resource optimization based on cell characteristic parameters;

[0008] The relevant information is sent to a near real-time wireless access network intelligent controller, so that the near real-time wireless access network intelligent controller configures a wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation.

[0009] The sending of the relevant information to the near real-time wireless access network intelligent controller includes:

[0010] The relevant information is sent to the near real-time wireless access network intelligent controller via the A1 interface.

[0011] The relevant information is as follows:

[0012] The basis for cell classification, or the result of cell classification, or the parameters used to customize the configuration of the wireless resource optimization algorithm.

[0013] The cell classification is based on: a cell classification standard, and / or a cell classification model, and / or a cell clustering model.

[0014] Wherein, when the relevant information is a basis for cell classification, the generating of relevant information for configuring wireless resource optimization includes:

[0015] Configure the cell classification criteria; or,

[0016] The training generates a cell classification model and / or a cell clustering model.

[0017] The input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and neighboring cell transmission power, cell and neighboring cell antenna parameters, cell and neighboring cell frequency bands, cell and neighboring cell site locations;

[0018] The output of the cell classification model is one or more of the following: cell coverage type, hetero-frequency included coverage cell, co-frequency overlapping coverage cell, hetero-frequency overlapping coverage cell;

[0019] The input of the cell clustering model is one or more of the following: cell service characteristic parameters, traffic volume of each service type, time interval between arrival 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;

[0020] The output of the cell clustering model is one or more of the following: different clustered cell service categories.

[0021] Wherein, when the relevant information is a cell classification result, the generating of the relevant information for configuring the wireless resource optimization includes:

[0022] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0023] Based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters, the category to which the cell belongs is determined.

[0024] The cell category includes but is not limited to the cell coverage category and / or the cell service category;

[0025] The cell coverage category includes one or more of the following:

[0026] Same-frequency shared coverage, different-frequency included coverage, same-frequency overlapping coverage, different-frequency overlapping coverage;

[0027] The cell service categories include: service category 1...service category k, where k service categories are generated by a clustering algorithm, each category carries a corresponding service feature profile, and the profile includes each service type ID, each service type's service volume ratio, each service type's average data packet size, and each service type's average data packet arrival rate.

[0028] Wherein, when the relevant information is a parameter for customizing the configuration of the wireless resource optimization algorithm, the generating of the relevant information for configuring the wireless resource optimization includes:

[0029] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0030] Determine the category to which the cell belongs based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters;

[0031] A wireless resource optimization algorithm parameter is determined based on the category to which the cell belongs.

[0032] The wireless resource optimization algorithm parameters include one or more of the following:

[0033] Monitoring indicators or combined monitoring indicators that trigger the wireless resource optimization algorithm, and the weights of each indicator;

[0034] Monitoring indicator or combined monitoring indicator trigger threshold;

[0035] Algorithm optimization goal;

[0036] Algorithm termination condition.

[0037] Optionally, the method further includes:

[0038] Acquire the cell characteristic parameters.

[0039] The cell characteristic parameters include: base station side cell characteristic parameters and network management side cell characteristic parameters;

[0040] The base station side cell characteristic parameters include but are not limited to one or more of the following:

[0041] Service type identification ID, service volume of each service type, time interval between arrival 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;

[0042] The network management side cell characteristic parameters include but are not limited to one or more of the following:

[0043] The cell's neighboring cell list, the cell and neighboring cell transmit power, the cell and neighboring cell antenna parameters, the cell and neighboring cell frequency bands, and the cell and neighboring cell site locations.

[0044] An embodiment of the present invention further provides a wireless resource optimization method, which is applied to a near real-time wireless access network intelligent controller, including:

[0045] Receiving relevant information for configuring wireless resource optimization;

[0046] A wireless resource optimization algorithm is configured based on the relevant information, and the base station finally completes the wireless resource optimization operation.

[0047] The receiving of relevant information for configuring wireless resource optimization includes:

[0048] The relevant information sent by the non-real-time wireless access network intelligent controller is received through the A1 interface.

[0049] The relevant information is as follows:

[0050] The basis for cell classification, or the result of cell classification, or the parameters used to customize the infinite resource optimization algorithm.

[0051] Wherein, when the relevant information is a basis for cell classification, configuring a wireless resource optimization algorithm based on the relevant information includes:

[0052] Determine the category to which the cell belongs based on the cell classification basis, the cell characteristic parameters on the base station side collected in real time by the E2 interface, and the cell characteristic parameters on the network management side received by the A1 interface;

[0053] Generate radio resource optimization algorithm parameters based on the category to which the cell belongs;

[0054] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0055] Wherein, when the relevant information is a cell classification result, configuring a wireless resource optimization algorithm based on the relevant information includes:

[0056] Generate radio resource optimization algorithm parameters based on the received cell category;

[0057] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0058] Wherein, when the relevant information is a parameter for customizing the configuration of a wireless resource optimization algorithm, configuring the wireless resource optimization algorithm based on the relevant information includes:

[0059] A wireless resource optimization algorithm is configured based on the received wireless resource optimization algorithm parameters.

[0060] The embodiment of the present invention further provides a wireless resource optimization device, which is applied to a non-real-time wireless access network intelligent controller, including:

[0061] A generating module, used to generate relevant information for configuring wireless resource optimization based on cell characteristic parameters;

[0062] The sending module is used to send the relevant information to the near real-time wireless access network intelligent controller, so that the near real-time wireless access network intelligent controller configures the wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation.

[0063] An embodiment of the present invention further provides a wireless resource optimization device, which is applied to a near real-time wireless access network intelligent controller, including:

[0064] A receiving module, used to receive relevant information for configuring wireless resource optimization;

[0065] The configuration module is used to configure the wireless resource optimization algorithm based on the relevant information, and the base station finally completes the wireless resource optimization operation.

[0066] The embodiment of the present invention further provides a wireless resource optimization device, the device comprising: a processor and a memory for storing a computer program that can be run on the processor,

[0067] Wherein, the processor is used to execute the steps of the above method when running the computer program.

[0068] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0069] The wireless resource optimization method, device and computer-readable storage medium provided by the embodiments of the present invention, the non-real-time wireless access network intelligent controller generates relevant information for configuring wireless resource optimization based on cell characteristic parameters; sends the relevant information to the near-real-time wireless access network intelligent controller, which is used by the near-real-time wireless access network intelligent controller to configure the wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation. The embodiments of the present invention customize the algorithm parameter configuration for the cell in the relevant information, adapt to different scenarios more finely, and trigger the wireless resource optimization algorithm more accurately, which can improve the traditional network management configuration method that indiscriminately uses a unified standard to configure the same algorithm parameters for all cells, which makes the wireless resource optimization performance worse. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 The wireless resource optimization method according to the embodiment of the present invention is shown in FIG. Figure 1 ;

[0071] Figure 2 The wireless resource optimization method according to the embodiment of the present invention is shown in FIG. Figure 2 ;

[0072] Figure 3 The structure of the wireless resource optimization device according to the embodiment of the present invention is shown in FIG. Figure 1 ;

[0073] Figure 4 The structure of the wireless resource optimization device according to the embodiment of the present invention is shown in FIG. Figure 2 ;

[0074] Figure 5 This is a schematic diagram of a radio resource optimization method based on an O-RAN architecture according to a first embodiment of the present invention;

[0075] Figure 6 This is a schematic diagram of a radio resource optimization method based on an O-RAN architecture according to a second scenario embodiment of the present invention;

[0076] Figure 7 This is a schematic diagram of a wireless resource optimization method based on an O-RAN architecture as described in the third scenario embodiment of the present invention. DETAILED DESCRIPTION

[0077] The present invention is described below in conjunction with the accompanying drawings and embodiments.

[0078] The embodiment of the present invention provides a method for optimizing wireless resources. Figure 1 As shown, the method is applied to a non-real-time wireless access network intelligent controller, comprising:

[0079] Step 101: Generate relevant information for configuring wireless resource optimization based on cell characteristic parameters;

[0080] Step 102: Send the relevant information to the near real-time wireless access network intelligent controller, so that the near real-time wireless access network intelligent controller configures a wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation.

[0081] The embodiments of the present invention customize the algorithm parameter configuration for the cell according to the relevant information, adapt to different scenarios more finely, and trigger the wireless resource optimization algorithm more accurately, which can improve the shortcomings of the traditional network management configuration method that indiscriminately adopts a unified standard to configure the same algorithm parameters for all cells, thereby deteriorating the wireless resource optimization performance.

[0082] In the embodiment of the present invention, the sending of the relevant information to the near real-time wireless access network intelligent controller includes:

[0083] The relevant information is sent to the near real-time wireless access network intelligent controller via the A1 interface.

[0084] In the embodiment of the present invention, the relevant information is:

[0085] The basis for cell classification, or the result of cell classification, or the parameters used to customize the configuration of the wireless resource optimization algorithm.

[0086] In the embodiment of the present invention, the cell classification is based on: a cell classification standard, and / or a cell classification model, and / or a cell clustering model.

[0087] In one embodiment of the present invention, when the relevant information is a basis for cell classification, the generating of the relevant information for configuring wireless resource optimization includes:

[0088] Configure the cell classification criteria; or,

[0089] The training generates a cell classification model and / or a cell clustering model.

[0090] In the embodiment of the present invention,

[0091] The input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and neighboring cell transmission power, cell and neighboring cell antenna parameters, cell and neighboring cell frequency bands, cell and neighboring cell site locations;

[0092] The output of the cell classification model is one or more of the following: cell coverage type, hetero-frequency included coverage cell, co-frequency overlapping coverage cell, hetero-frequency overlapping coverage cell;

[0093] The input of the cell clustering model is one or more of the following: cell service characteristic parameters, traffic volume of each service type, time interval between arrival 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;

[0094] The output of the cell clustering model is one or more of the following: different clustered cell service categories.

[0095] In one embodiment of the present invention, when the relevant information is a cell classification result, generating the relevant information for configuring wireless resource optimization includes:

[0096] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0097] Based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters, the category to which the cell belongs is determined.

[0098] In the embodiment of the present invention, the cell category includes but is not limited to the cell coverage category and / or the cell service category; wherein,

[0099] The cell coverage category includes one or more of the following:

[0100] Same-frequency shared coverage, different-frequency included coverage, same-frequency overlapping coverage, different-frequency overlapping coverage;

[0101] The cell service categories include: service category 1...service category k, where k service categories are generated by a clustering algorithm, each category carries a corresponding service feature profile, and the profile includes each service type ID, each service type's service volume ratio, each service type's average data packet size, and each service type's average data packet arrival rate.

[0102] In one embodiment of the present invention, when the relevant information is a parameter for customizing the configuration of a wireless resource optimization algorithm, the generating of the relevant information for configuring wireless resource optimization includes:

[0103] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0104] Determine the category to which the cell belongs based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters;

[0105] A wireless resource optimization algorithm parameter is determined based on the category to which the cell belongs.

[0106] In the embodiment of the present invention, the radio resource optimization algorithm parameters include one or more of the following:

[0107] Monitoring indicators or combined monitoring indicators that trigger the wireless resource optimization algorithm, and the weights of each indicator;

[0108] Monitoring indicator or combined monitoring indicator trigger threshold;

[0109] Algorithm optimization goal;

[0110] Algorithm termination condition.

[0111] In one embodiment of the present invention, the method further comprises:

[0112] Acquire the cell characteristic parameters.

[0113] 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,

[0114] The base station side cell characteristic parameters include but are not limited to one or more of the following:

[0115] Service type identification ID, service volume of each service type, time interval between arrival 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;

[0116] The network management side cell characteristic parameters include but are not limited to one or more of the following:

[0117] The cell's neighboring cell list, the cell and neighboring cell transmit power, the cell and neighboring cell antenna parameters, the cell and neighboring cell frequency bands, and the cell and neighboring cell site locations.

[0118] The embodiment of the present invention also provides a method for optimizing wireless resources. Figure 2 As shown, the method is applied to a near real-time wireless access network intelligent controller, comprising:

[0119] Step 201: Receive relevant information for configuring wireless resource optimization;

[0120] Step 202: A wireless resource optimization algorithm is configured based on the relevant information, and the base station finally completes the wireless resource optimization operation.

[0121] In the embodiment of the present invention, the receiving of relevant information for configuring wireless resource optimization includes:

[0122] The relevant information sent by the non-real-time wireless access network intelligent controller is received through the A1 interface.

[0123] In the embodiment of the present invention, the relevant information is:

[0124] The basis for cell classification, or the result of cell classification, or the parameters used to customize the infinite resource optimization algorithm.

[0125] In one embodiment of the present invention, when the relevant information is a basis for cell classification, configuring a wireless resource optimization algorithm based on the relevant information includes:

[0126] Determine the category to which the cell belongs based on the cell classification basis, the cell characteristic parameters on the base station side collected in real time by the E2 interface, and the cell characteristic parameters on the network management side received by the A1 interface;

[0127] Generate radio resource optimization algorithm parameters based on the category to which the cell belongs;

[0128] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0129] In one embodiment of the present invention, when the relevant information is a cell classification result, configuring a wireless resource optimization algorithm based on the relevant information includes:

[0130] Generate radio resource optimization algorithm parameters based on the received cell category;

[0131] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0132] In another embodiment of the present invention, when the relevant information is a parameter for customizing the configuration of a wireless resource optimization algorithm, configuring the wireless resource optimization algorithm based on the relevant information includes:

[0133] A wireless resource optimization algorithm is configured based on the received wireless resource optimization algorithm parameters.

[0134] In order to implement the above method embodiment, the embodiment of the present invention also provides a wireless resource optimization device, such as Figure 3 As shown, the device is applied to a non-real-time wireless access network intelligent controller, including:

[0135] A generating module 301, configured to generate relevant information for configuring wireless resource optimization based on cell characteristic parameters;

[0136] The sending module 302 is used to send the relevant information to the near real-time wireless access network intelligent controller, so that the near real-time wireless access network intelligent controller configures a wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation.

[0137] In the embodiment of the present invention, the sending module 302 sends the relevant information to the near real-time wireless access network intelligent controller, including:

[0138] The relevant information is sent to the near real-time wireless access network intelligent controller via the A1 interface.

[0139] In the embodiment of the present invention, the relevant information is:

[0140] The basis for cell classification, or the result of cell classification, or the parameters used to customize the configuration of the wireless resource optimization algorithm.

[0141] In the embodiment of the present invention, the cell classification is based on: a cell classification standard, and / or a cell classification model, and / or a cell clustering model.

[0142] In one embodiment of the present invention, when the relevant information is a basis for cell classification, the generating module 301 generates relevant information for configuring wireless resource optimization, including:

[0143] Configure the cell classification criteria; or,

[0144] The training generates a cell classification model and / or a cell clustering model.

[0145] In the embodiment of the present invention,

[0146] The input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and neighboring cell transmission power, cell and neighboring cell antenna parameters, cell and neighboring cell frequency bands, cell and neighboring cell site locations;

[0147] The output of the cell classification model is one or more of the following: cell coverage type, hetero-frequency included coverage cell, co-frequency overlapping coverage cell, hetero-frequency overlapping coverage cell;

[0148] The input of the cell clustering model is one or more of the following: cell service characteristic parameters, traffic volume of each service type, time interval between arrival 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;

[0149] The output of the cell clustering model is one or more of the following: different clustered cell service categories.

[0150] In one embodiment of the present invention, when the relevant information is a cell classification result, the generating module 301 generates relevant information for configuring wireless resource optimization, including:

[0151] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0152] Based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters, the category to which the cell belongs is determined.

[0153] In the embodiment of the present invention, the cell category includes but is not limited to the cell coverage category and / or the cell service category; wherein,

[0154] The cell coverage category includes one or more of the following:

[0155] Same-frequency shared coverage, different-frequency included coverage, same-frequency overlapping coverage, different-frequency overlapping coverage;

[0156] The cell service categories include: service category 1...service category k, where k service categories are generated by a clustering algorithm, each category carries a corresponding service feature profile, and the profile includes each service type ID, each service type's service volume ratio, each service type's average data packet size, and each service type's average data packet arrival rate.

[0157] In one embodiment of the present invention, when the relevant information is a parameter for customizing the configuration of a wireless resource optimization algorithm, the generating module 301 generates relevant information for configuring wireless resource optimization, including:

[0158] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0159] Determine the category to which the cell belongs based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters;

[0160] A wireless resource optimization algorithm parameter is determined based on the category to which the cell belongs.

[0161] In the embodiment of the present invention, the radio resource optimization algorithm parameters include one or more of the following:

[0162] Monitoring indicators or combined monitoring indicators that trigger the wireless resource optimization algorithm, and the weights of each indicator;

[0163] Monitoring indicator or combined monitoring indicator trigger threshold;

[0164] Algorithm optimization goal;

[0165] Algorithm termination condition.

[0166] In one embodiment of the present invention, the generating module 301 is further used to obtain the cell characteristic parameters.

[0167] 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,

[0168] The base station side cell characteristic parameters include but are not limited to one or more of the following:

[0169] Service type identification ID, service volume of each service type, time interval between arrival 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;

[0170] The network management side cell characteristic parameters include but are not limited to one or more of the following:

[0171] The cell's neighboring cell list, the cell and neighboring cell transmit power, the cell and neighboring cell antenna parameters, the cell and neighboring cell frequency bands, and the cell and neighboring cell site locations.

[0172] The embodiment of the present invention also provides a wireless resource optimization device, such as Figure 4 As shown, the device is applied to a near real-time wireless access network intelligent controller, including:

[0173] The receiving module 401 is used to receive relevant information for configuring wireless resource optimization;

[0174] The configuration module 402 is used to configure a wireless resource optimization algorithm based on the relevant information, and the base station finally completes the wireless resource optimization operation.

[0175] In the embodiment of the present invention, the receiving module 401 receives relevant information for configuring wireless resource optimization, including:

[0176] The relevant information sent by the non-real-time wireless access network intelligent controller is received through the A1 interface.

[0177] In the embodiment of the present invention, the relevant information is:

[0178] The basis for cell classification, or the result of cell classification, or the parameters used to customize the infinite resource optimization algorithm.

[0179] In one embodiment of the present invention, when the relevant information is a basis for cell classification, the configuration module 402 configures a wireless resource optimization algorithm based on the relevant information, including:

[0180] Determine the category to which the cell belongs based on the cell classification basis, the cell characteristic parameters on the base station side collected in real time by the E2 interface, and the cell characteristic parameters on the network management side received by the A1 interface;

[0181] Generate radio resource optimization algorithm parameters based on the category to which the cell belongs;

[0182] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0183] In one embodiment of the present invention, when the relevant information is a cell classification result, the configuration module 402 configures a wireless resource optimization algorithm based on the relevant information, including:

[0184] Generate radio resource optimization algorithm parameters based on the received cell category;

[0185] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0186] In another embodiment of the present invention, when the relevant information is a parameter for customizing the configuration of the wireless resource optimization algorithm, the configuration module 402 configures the wireless resource optimization algorithm based on the relevant information, including:

[0187] A wireless resource optimization algorithm is configured based on the received wireless resource optimization algorithm parameters.

[0188] The embodiment of the present invention further provides a wireless resource optimization device, which is applied to a non-real-time wireless access network intelligent controller, and includes: a processor and a memory for storing a computer program that can be run on the processor,

[0189] Wherein, when the processor is used to run the computer program, it executes:

[0190] Generate relevant information for configuring wireless resource optimization based on cell characteristic parameters;

[0191] The relevant information is sent to a near real-time wireless access network intelligent controller, so that the near real-time wireless access network intelligent controller configures a wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation.

[0192] When the related information is sent to the near real-time wireless access network intelligent controller, the processor is further configured to execute:

[0193] The relevant information is sent to the near real-time wireless access network intelligent controller via the A1 interface.

[0194] The relevant information is as follows:

[0195] The basis for cell classification, or the result of cell classification, or the parameters used to customize the configuration of the wireless resource optimization algorithm.

[0196] The cell classification is based on: a cell classification standard, and / or a cell classification model, and / or a cell clustering model.

[0197] When the relevant information is a basis for cell classification, when the relevant information for configuring wireless resource optimization is generated, the processor is further configured to execute:

[0198] Configure the cell classification criteria; or,

[0199] The training generates a cell classification model and / or a cell clustering model.

[0200] The input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and neighboring cell transmission power, cell and neighboring cell antenna parameters, cell and neighboring cell frequency bands, cell and neighboring cell site locations;

[0201] The output of the cell classification model is one or more of the following: cell coverage type, hetero-frequency included coverage cell, co-frequency overlapping coverage cell, hetero-frequency overlapping coverage cell;

[0202] The input of the cell clustering model is one or more of the following: cell service characteristic parameters, traffic volume of each service type, time interval between arrival 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;

[0203] The output of the cell clustering model is one or more of the following: different clustered cell service categories.

[0204] When the relevant information is a cell classification result, when the relevant information for configuring wireless resource optimization is generated, the processor is further configured to execute:

[0205] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0206] Based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters, the category to which the cell belongs is determined.

[0207] The cell category includes but is not limited to the cell coverage category and / or the cell service category;

[0208] The cell coverage category includes one or more of the following:

[0209] Same-frequency shared coverage, different-frequency included coverage, same-frequency overlapping coverage, different-frequency overlapping coverage;

[0210] The cell service categories include: service category 1...service category k, where k service categories are generated by a clustering algorithm, each category carries a corresponding service feature profile, and the profile includes each service type ID, each service type's service volume ratio, each service type's average data packet size, and each service type's average data packet arrival rate.

[0211] When the relevant information is a parameter for customizing the configuration of the wireless resource optimization algorithm, when the relevant information for configuring the wireless resource optimization is generated, the processor is further configured to execute:

[0212] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0213] Determine the category to which the cell belongs based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters;

[0214] A wireless resource optimization algorithm parameter is determined based on the category to which the cell belongs.

[0215] The wireless resource optimization algorithm parameters include one or more of the following:

[0216] Monitoring indicators or combined monitoring indicators that trigger the wireless resource optimization algorithm, and the weights of each indicator;

[0217] Monitoring indicator or combined monitoring indicator trigger threshold;

[0218] Algorithm optimization goal;

[0219] Algorithm termination condition.

[0220] The processor is further configured to execute, when running the computer program:

[0221] Acquire the cell characteristic parameters.

[0222] The cell characteristic parameters include: base station side cell characteristic parameters and network management side cell characteristic parameters;

[0223] The base station side cell characteristic parameters include but are not limited to one or more of the following:

[0224] Service type identification ID, service volume of each service type, time interval between arrival 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;

[0225] The network management side cell characteristic parameters include but are not limited to one or more of the following:

[0226] The cell's neighboring cell list, the cell and neighboring cell transmit power, the cell and neighboring cell antenna parameters, the cell and neighboring cell frequency bands, and the cell and neighboring cell site locations.

[0227] The embodiment of the present invention further provides a wireless resource optimization device, which is applied to a near real-time wireless access network intelligent controller, comprising: a processor and a memory for storing a computer program that can be run on the processor,

[0228] Wherein, when the processor is used to run the computer program, it executes:

[0229] Receiving relevant information for configuring wireless resource optimization;

[0230] A wireless resource optimization algorithm is configured based on the relevant information, and the base station finally completes the wireless resource optimization operation.

[0231] When the receiving of the relevant information for configuring the optimization of the wireless resources, the processor is further configured to execute:

[0232] The relevant information sent by the non-real-time wireless access network intelligent controller is received through the A1 interface.

[0233] The relevant information is as follows:

[0234] The basis for cell classification, or the result of cell classification, or the parameters used to customize the infinite resource optimization algorithm.

[0235] When the relevant information is a basis for cell classification, and when the wireless resource optimization algorithm is configured based on the relevant information, the processor is further configured to execute, when running the computer program:

[0236] Determine the category to which the cell belongs based on the cell classification basis, the cell characteristic parameters on the base station side collected in real time by the E2 interface, and the cell characteristic parameters on the network management side received by the A1 interface;

[0237] Generate radio resource optimization algorithm parameters based on the category to which the cell belongs;

[0238] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0239] When the relevant information is a cell classification result, when the wireless resource optimization algorithm is configured based on the relevant information, the processor is further configured to execute, when running the computer program:

[0240] Generate radio resource optimization algorithm parameters based on the received cell category;

[0241] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0242] When the relevant information is a parameter for customizing and configuring a wireless resource optimization algorithm, when configuring the wireless resource optimization algorithm based on the relevant information, the processor is further configured to execute, when running the computer program:

[0243] A wireless resource optimization algorithm is configured based on the received wireless resource optimization algorithm parameters.

[0244] It should be noted that: when the device provided in the above embodiment performs wireless resource optimization, only the division of the above program modules is used as an example. In actual applications, the above processing can be assigned to different program modules as needed, 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 device provided in the above embodiment and the corresponding method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0245] In an exemplary embodiment, an embodiment of the present invention further provides a computer-readable storage medium, which may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc.

[0246] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the computer program performs:

[0247] Generate relevant information for configuring wireless resource optimization based on cell characteristic parameters;

[0248] The relevant information is sent to a near real-time wireless access network intelligent controller, so that the near real-time wireless access network intelligent controller configures a wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation.

[0249] When the relevant information is sent to the near real-time wireless access network intelligent controller, the computer program is executed by the processor and further executes:

[0250] The relevant information is sent to the near real-time wireless access network intelligent controller via the A1 interface.

[0251] The relevant information is as follows:

[0252] The basis for cell classification, or the result of cell classification, or the parameters used to customize the configuration of the wireless resource optimization algorithm.

[0253] The cell classification is based on: a cell classification standard, and / or a cell classification model, and / or a cell clustering model.

[0254] When the relevant information is a basis for cell classification, and when the relevant information for configuring wireless resource optimization is generated, when the computer program is executed by the processor, the computer program further executes:

[0255] Configure the cell classification criteria; or,

[0256] The training generates a cell classification model and / or a cell clustering model.

[0257] The input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and neighboring cell transmission power, cell and neighboring cell antenna parameters, cell and neighboring cell frequency bands, cell and neighboring cell site locations;

[0258] The output of the cell classification model is one or more of the following: cell coverage type, hetero-frequency included coverage cell, co-frequency overlapping coverage cell, hetero-frequency overlapping coverage cell;

[0259] The input of the cell clustering model is one or more of the following: cell service characteristic parameters, traffic volume of each service type, time interval between arrival 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;

[0260] The output of the cell clustering model is one or more of the following: different clustered cell service categories.

[0261] When the relevant information is a cell classification result, and when the relevant information for configuring wireless resource optimization is generated, when the computer program is executed by the processor, the computer program further executes:

[0262] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0263] Based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters, the category to which the cell belongs is determined.

[0264] The cell category includes but is not limited to the cell coverage category and / or the cell service category;

[0265] The cell coverage category includes one or more of the following:

[0266] Same-frequency shared coverage, different-frequency included coverage, same-frequency overlapping coverage, different-frequency overlapping coverage;

[0267] The cell service categories include: service category 1...service category k, where k service categories are generated by a clustering algorithm, each category carries a corresponding service feature profile, and the profile includes each service type ID, each service type's service volume ratio, each service type's average data packet size, and each service type's average data packet arrival rate.

[0268] When the relevant information is a parameter for customizing the configuration of the wireless resource optimization algorithm, when the relevant information for configuring the wireless resource optimization is generated, when the computer program is executed by the processor, the computer program further executes:

[0269] Configure cell classification standards, or train and generate cell classification models and / or cell clustering models;

[0270] Determine the category to which the cell belongs based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters;

[0271] A wireless resource optimization algorithm parameter is determined based on the category to which the cell belongs.

[0272] The wireless resource optimization algorithm parameters include one or more of the following:

[0273] Monitoring indicators or combined monitoring indicators that trigger the wireless resource optimization algorithm, and the weights of each indicator;

[0274] Monitoring indicator or combined monitoring indicator trigger threshold;

[0275] Algorithm optimization goal;

[0276] Algorithm termination condition.

[0277] When the computer program is executed by a processor, it also performs:

[0278] Acquire the cell characteristic parameters.

[0279] The cell characteristic parameters include: base station side cell characteristic parameters and network management side cell characteristic parameters;

[0280] The base station side cell characteristic parameters include but are not limited to one or more of the following:

[0281] Service type identification ID, service volume of each service type, time interval between arrival 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;

[0282] The network management side cell characteristic parameters include but are not limited to one or more of the following:

[0283] The cell's neighboring cell list, the cell and neighboring cell transmit power, the cell and neighboring cell antenna parameters, the cell and neighboring cell frequency bands, and the cell and neighboring cell site locations.

[0284] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the computer program performs:

[0285] Receiving relevant information for configuring wireless resource optimization;

[0286] A wireless resource optimization algorithm is configured based on the relevant information, and the base station finally completes the wireless resource optimization operation.

[0287] When the relevant information for configuring the optimization of wireless resources is received, when the computer program is executed by the processor, the computer program further executes:

[0288] The relevant information sent by the non-real-time wireless access network intelligent controller is received through the A1 interface.

[0289] The relevant information is as follows:

[0290] The basis for cell classification, or the result of cell classification, or the parameters used to customize the infinite resource optimization algorithm.

[0291] When the relevant information is a basis for cell classification, and when the wireless resource optimization algorithm is configured based on the relevant information, when the computer program is executed by the processor, the following steps are further performed:

[0292] Determine the category to which the cell belongs based on the cell classification basis, the cell characteristic parameters on the base station side collected in real time by the E2 interface, and the cell characteristic parameters on the network management side received by the A1 interface;

[0293] Generate radio resource optimization algorithm parameters based on the category to which the cell belongs;

[0294] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0295] When the relevant information is a cell classification result, and when the wireless resource optimization algorithm is configured based on the relevant information, when the computer program is executed by the processor, the following steps are further performed:

[0296] Generate radio resource optimization algorithm parameters based on the received cell category;

[0297] A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

[0298] When the relevant information is a parameter for customizing and configuring a wireless resource optimization algorithm, when configuring the wireless resource optimization algorithm based on the relevant information, the computer program, when executed by the processor, further executes:

[0299] A wireless resource optimization algorithm is configured based on the received wireless resource optimization algorithm parameters.

[0300] The present invention is described below in conjunction with scenario embodiments.

[0301] As wireless network coverage scenarios and the types of services provided and the distribution of service types become more and more diverse, the cell characteristics presented by cells are often different. For example, most of the services accessed by cell A are eMBB services, while most of the services accessed by cell B are IoT services. The service characteristics of these two services, such as packet size and packet arrival time interval, are different, so the cell service characteristics presented by cells A and B are also different. Accordingly, the parameters of the wireless resource optimization algorithm of different cells should be customized 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 is shorter than that of cell B. Therefore, for the parameters of the cell load balancing algorithm (a wireless resource optimization algorithm), such as the cell load monitoring period, the load monitoring period of cell A should be set shorter than that of cell B.

[0302] Therefore, it is necessary to first classify the cells more finely according to their characteristic parameters, and then generate and match the parameters of the cell wireless resource optimization algorithm in a customized manner based on the cell categories.

[0303] Specifically, this embodiment proposes a method for optimizing wireless resources based on cell categories based on an open radio access network (O-RAN) two-level intelligent controller (non-real-time RAN intelligent controller Non-RT RIC, near-real-time radio access network intelligent controller Near-RT RIC) architecture. According to the different distribution of functions between the two-level RIC, three corresponding solutions are provided through the following three embodiments.

[0304] Embodiment 1:

[0305] In this embodiment, the A1 interface sends the cell classification result, such as Figure 5 As shown, including:

[0306] Step 1: The network management / Non-RT RIC collects cell characteristic parameters on the base station side through the O1 interface, such as service type ID, service volume of each service type, time interval between arrival of data packets of each service type, size of data packets of each service type, arrival rate of data packets of each service type, etc.

[0307] Step 2: Combined with the historical cell characteristic parameters on the base station side and the network management side, the network management / Non-RT RIC formulates the cell classification standard, or trains the cell classification and / or cell clustering model, and determines the cell category based on the current cell characteristic parameters and sends the cell category to the Near-RT RIC through the A1 interface. Among them, the cell characteristic parameters on the network management side include: the cell's neighboring cell list, the cell and neighboring cell transmission power, the cell and neighboring cell antenna parameters, the cell and neighboring cell frequency bands, the cell and neighboring cell site locations, etc.; the cell category (i.e., the cell category) sent down by the A1 interface includes but is not limited to: cell coverage category and / or cell service category, among which:

[0308] The cell coverage categories include: same-frequency co-coverage, different-frequency included coverage, same-frequency overlapping coverage, different-frequency overlapping coverage, etc.;

[0309] The cell service categories include: service category 1...service category k, wherein k service categories are generated by a clustering algorithm, each category 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;

[0310] Step 3: Near-RT RIC generates radio resource optimization algorithm parameters and configures the radio resource optimization algorithm based on the received cell category;

[0311] Step 4: The configured wireless resource optimization algorithm will generate intelligent wireless resource optimization control based on the cell-level / user-level real-time measurement parameters reported by the E2 interface and send it to the base station. The base station will finally complete the wireless resource optimization operation.

[0312] Embodiment 2:

[0313] In this embodiment, the A1 interface sends a cell classification model / cell clustering model / cell classification standard, such as Figure 6 As shown, including:

[0314] Step 1: According to the collected cell characteristic parameters (same as in Embodiment 1), the network management / Non-RT RIC formulates a cell classification standard, or trains a cell classification and / or cell clustering model, and sends the cell classification model and / or cell clustering model and / or cell classification standard and the cell characteristic parameters on the network management side to the Near-RT RIC through the A1 interface, so that the Near-RT RIC completes the cell classification with stronger real-time performance; wherein,

[0315] For the cell classification model:

[0316] The input is: cell coverage characteristic parameters such as the cell's neighboring cell list, the cell and neighboring cell transmission power, the cell and neighboring cell antenna parameters, the cell and neighboring cell frequency bands, the cell and neighboring cell site locations, etc.

[0317] The output is: the cell coverage types output by the cell classification model include same-frequency shared coverage cells, different-frequency included coverage cells, same-frequency overlapping coverage cells, different-frequency overlapping coverage cells, etc.

[0318] For the cell clustering model:

[0319] Its input is: cell service characteristic parameters such as service type ID, service volume of each service type, time interval between arrival 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 multi-dimensional service characteristics;

[0320] The output is: different cell service categories clustered by a cell clustering model (such as K-means), such as service category 1, service category 2, ... service category k, where each category corresponds to a different profile, and the profile may include each service type ID, the proportion of service volume of each service type, the average data packet size of each service type, and the average data packet arrival rate of each service type.

[0321] The cell classification standard includes but is not limited to classifying into large packet cells, medium packet cells, small packet cells, etc. according to the data characteristics of the cell service data packet size;

[0322] Step 2: Near-RT RIC collects cell characteristic parameters on the base station side in real time through the E2 interface according to the received cell classification / cell clustering model / cell classification standard, and receives cell characteristic parameters on the network management side through the A1 interface, and determines the cell category based on the collected real-time cell characteristic parameters;

[0323] Step 3: Generate wireless resource optimization algorithm parameters based on cell categories and configure the wireless resource optimization algorithm. The configured wireless resource optimization algorithm will generate intelligent wireless resource optimization control and send it to the base station based on the cell-level / user-level real-time measurement parameters reported by the E2 interface. The base station will finally complete the wireless resource optimization operation.

[0324] Embodiment three:

[0325] In this embodiment, the A1 interface sends the cell radio resource optimization algorithm configuration parameters, such as Figure 7 As shown, including:

[0326] Step 1: According to the collected cell characteristic parameters (same as in Example 1), the network management / Non-RT RIC formulates a cell classification standard, or trains a cell classification and / or cell clustering model, and determines the category to which the cell belongs according to the real-time cell characteristic parameters, determines the wireless resource optimization algorithm parameters based on the cell category, and sends them to the Near-RT RIC through the A1 interface. The wireless resource optimization algorithm parameters sent by the A1 interface include:

[0327] 1) Monitoring indicators / combined monitoring indicators and weights of various indicators that trigger the wireless resource optimization algorithm:

[0328] For example: PRB utilization rate, PDCCH CCE utilization rate, RRC connection rate, number of users, etc. for data transmission; different cell service categories will also present different characteristics for the occupation of control channel resources and data channel resources in the cell, so the triggering conditions for triggering different cell resource optimization algorithms should be dynamically configured;

[0329] In addition to configuring the above single monitoring indicators, you can also configure combined monitoring indicators and the weights of each indicator (for example, 60%*PRB utilization for data transmission + 40%*PDCCH CCE utilization). The combined monitoring indicator can effectively avoid the algorithm triggering accuracy of a single indicator, which is more in line with the evaluation of cell load in different business scenarios (such as ultra-wideband business and wide connection business).

[0330] 2) Monitoring indicator / combined monitoring indicator trigger threshold:

[0331] For example: PRB total occupancy threshold: 70%; user number threshold: 200; combined threshold of 60%*PRB utilization for data transmission + 40%*PDCCH CCE utilization: 70%;

[0332] 3) Algorithm optimization objectives:

[0333] For example: minimizing the load variance between the serving cell and the neighboring cell, minimizing the number of user switching times, minimizing the user rate loss, achieving the user guaranteed rate, and combined optimization goals based on the above single optimization goals (such as min (load variance between the serving cell and the neighboring cell + number of user switching times + user rate loss)).

[0334] 4) Algorithm termination condition:

[0335] For example: the user experience rate reaches the specified guaranteed value, or the cell load drops to the specified threshold, etc.

[0336] Step 2: Near-RT RIC configures the wireless resource optimization algorithm according to the received cell wireless resource optimization algorithm parameters. The configured wireless resource optimization algorithm generates intelligent wireless resource optimization control according to the cell-level / user-level real-time measurement parameters reported by the E2 interface and sends it to the base station. The base station finally completes the wireless resource optimization operation.

[0337] Embodiment 4

[0338] This embodiment provides a specific application scenario, which is as follows:

[0339] Step 1: Non-RT RIC collects cell characteristic parameters on the base station side through the O1 interface. The cell characteristic parameters include:

[0340] Each business type ID;

[0341] Business volume of each business type;

[0342] The time interval between arrival of data packets of each service type;

[0343] Data packet size for each service type;

[0344] Packet arrival rate of each service type.

[0345] Step 2: Based on the collected cell service characteristic parameters, Non-RT RIC uses the Kmeans unsupervised learning model to train the cell clustering model, clustering the cell service categories, and obtain k categories. Each category will generate a corresponding profile (cell service category 1+profile 1, cell service category 2+profile 2...cell service category k+profile k). The profile contains:

[0346] The service type IDs belonging to this category (ID1: video, ID2: voice, ID3: IoT service);

[0347] The proportion of business volume of each business type (ID1: 70%, ID2: 20%, ID3: 10%);

[0348] Average data packet size of each service type (ID1: 1000Byte, ID2: 300Byte, ID3: 100bits);

[0349] Average data packet arrival rate for each service type (ID1: 5 packets / second, ID2: 2 packets / second, ID3: 15 packets / second).

[0350] Step 3: Through the clustering model for classifying the service category of the cell, the Non-RT RIC outputs the category and corresponding profile of the current cell according to the service characteristic parameters of the current cell. The Non-RT RIC parses the profile of the category of the current cell to generate wireless resource optimization algorithm parameters, and the wireless resource optimization algorithm parameters include:

[0351] The load balancing algorithm is triggered by a combination of indicators: PRB utilization for data transmission + PDCCH CCE utilization;

[0352] Combined indicator weight: 60%, 40%;

[0353] Combined indicator trigger threshold: 70%;

[0354] Algorithm optimization goal: to achieve user guaranteed rate;

[0355] Algorithm termination condition: The average user experience rate reaches 1000 bit / s.

[0356] Step 4: Send the generated radio resource optimization algorithm parameters to the Near-RT RIC through the A1 interface for configuring the load balancing algorithm;

[0357] Step 5: The load balancing algorithm runs according to the configured wireless resource optimization algorithm parameters to complete the wireless resource optimization operation.

[0358] The embodiment of the present invention generates relevant information for configuring wireless resource optimization based on cell characteristic parameters; and configures a wireless resource optimization algorithm based on the relevant information, performs customized algorithm parameter configuration on the cell according to the cell classification result in the relevant information, adapts to different scenarios more finely, and triggers the wireless resource optimization algorithm more accurately, which can improve the deficiency of the traditional network management configuration method that indiscriminately adopts a unified standard to configure the same algorithm parameters for all cells, thereby deteriorating the wireless resource optimization performance.

[0359] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A wireless resource optimization method, characterized in that: The method is applied to a non-real-time wireless access network intelligent controller, comprising: Generate relevant information for configuring wireless resource optimization based on cell characteristic parameters; Sending the relevant information to a near real-time wireless access network intelligent controller, so that the near real-time wireless access network intelligent controller configures a wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation; The relevant information is as follows: The basis for cell classification, or the result of cell classification, or the parameters used to customize the configuration of the wireless resource optimization algorithm.

2. The method according to claim 1, characterized in that The sending the relevant information to the near real-time wireless access network intelligent controller includes: The relevant information is sent to the near real-time wireless access network intelligent controller via the A1 interface.

3. The method according to claim 1, characterized in that The cell classification is based on: a cell classification standard, and / or a cell classification model, and / or a cell clustering model.

4. The method according to claim 3, characterized in that When the relevant information is a basis for cell classification, the generating of relevant information for configuring wireless resource optimization includes: Configure the cell classification criteria; or, The training generates a cell classification model and / or a cell clustering model.

5. The method according to claim 4, characterized in that The input of the cell classification model is one or more of the following: cell coverage characteristic parameters, cell and neighboring cell transmission power, cell and neighboring cell antenna parameters, cell and neighboring cell frequency bands, cell and neighboring cell site locations; The output of the cell classification model is one or more of the following: cell coverage type, hetero-frequency included coverage cell, co-frequency overlapping coverage cell, hetero-frequency overlapping coverage cell; The input of the cell clustering model is one or more of the following: cell service characteristic parameters, traffic volume of each service type, time interval between arrival 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: different clustered cell service categories.

6. The method according to claim 3, characterized in that When the relevant information is a cell classification result, the generating of the relevant information for configuring the wireless resource optimization includes: Configure cell classification standards, or train and generate cell classification models and / or cell clustering models; Based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters, the category to which the cell belongs is determined.

7. The method according to claim 6, characterized in that The cell category includes but is not limited to cell coverage category and / or cell service category; wherein, The cell coverage category includes one or more of the following: Same-frequency shared coverage, different-frequency included coverage, same-frequency overlapping coverage, different-frequency overlapping coverage; The cell service categories include: service category 1...service category k, where k service categories are generated by a clustering algorithm, each category carries a corresponding service feature profile, and the profile includes each service type ID, each service type's service volume ratio, each service type's average data packet size, and each service type's average data packet arrival rate.

8. The method according to claim 3, characterized in that When the relevant information is a parameter for customizing the configuration of a wireless resource optimization algorithm, the generating of the relevant information for configuring wireless resource optimization includes: Configure cell classification standards, or train and generate cell classification models and / or cell clustering models; Determine the category to which the cell belongs based on the cell classification standard and / or the cell classification model and / or the cell clustering model and the current cell characteristic parameters; A wireless resource optimization algorithm parameter is determined based on the category to which the cell belongs.

9. The method according to claim 8, characterized in that The wireless resource optimization algorithm parameters include one or more of the following: Monitoring indicators or combined monitoring indicators that trigger the wireless resource optimization algorithm, and the weights of each indicator; Monitoring indicator or combined monitoring indicator trigger threshold; Algorithm optimization goal; Algorithm termination condition.

10. The method according to claim 1, characterized in that The method further includes: Acquire the cell characteristic parameters.

11. The method according to claim 1 or 10, characterized in that: The cell characteristic parameters include: base station side cell characteristic parameters and network management side cell characteristic parameters; wherein, 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, time interval between arrival 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 cell's neighboring cell list, the cell and neighboring cell transmit power, the cell and neighboring cell antenna parameters, the cell and neighboring cell frequency bands, and the cell and neighboring cell site locations.

12. A wireless resource optimization method, characterized in that: The method is applied to a near real-time wireless access network intelligent controller, including: Receiving relevant information for configuring wireless resource optimization; A wireless resource optimization algorithm is configured based on the relevant information, and the base station finally completes the wireless resource optimization operation; The relevant information is as follows: The basis for cell classification, or the result of cell classification, or the parameters used to customize the infinite resource optimization algorithm.

13. The method according to claim 12, characterized in that The receiving of relevant information for configuring wireless resource optimization includes: The relevant information sent by the non-real-time wireless access network intelligent controller is received through the A1 interface.

14. The method according to claim 12, characterized in that When the relevant information is a basis for cell classification, configuring a wireless resource optimization algorithm based on the relevant information includes: Determine the category to which the cell belongs based on the cell classification basis, the cell characteristic parameters on the base station side collected in real time by the E2 interface, and the cell characteristic parameters on the network management side received by the A1 interface; Generate radio resource optimization algorithm parameters based on the category to which the cell belongs; A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

15. The method according to claim 12, characterized in that When the relevant information is a cell classification result, configuring a wireless resource optimization algorithm based on the relevant information includes: Generate radio resource optimization algorithm parameters based on the received cell category; A wireless resource optimization algorithm is configured based on the wireless resource optimization algorithm parameters.

16. The method according to claim 12, characterized in that When the relevant information is a parameter for customizing the configuration of the wireless resource optimization algorithm, configuring the wireless resource optimization algorithm based on the relevant information includes: A wireless resource optimization algorithm is configured based on the received wireless resource optimization algorithm parameters.

17. A wireless resource optimization device, characterized in that: The device is applied to a non-real-time wireless access network intelligent controller, including: A generating module, used to generate relevant information for configuring wireless resource optimization based on cell characteristic parameters; A sending module, used for sending the relevant information to a near real-time wireless access network intelligent controller, so that the near real-time wireless access network intelligent controller configures a wireless resource optimization algorithm, and the base station finally completes the wireless resource optimization operation; The relevant information is as follows: The basis for cell classification, or the result of cell classification, or the parameters used to customize the infinite resource optimization algorithm.

18. A wireless resource optimization device, characterized in that: The device is applied to a near real-time wireless access network intelligent controller, including: A receiving module, used to receive relevant information for configuring wireless resource optimization; A configuration module, configured to configure a wireless resource optimization algorithm based on the relevant information, and the base station finally completes the wireless resource optimization operation; The relevant information is as follows: The basis for cell classification, or the result of cell classification, or the parameters used to customize the infinite resource optimization algorithm.

19. A wireless resource optimization device, characterized in that: The apparatus comprises: a processor and a memory for storing a computer program capable of running on the processor, Wherein, when the processor is used to run the computer program, it executes the steps of the method described in any one of claims 1-11, or executes the steps of the method described in any one of claims 12-16.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program implements the steps of the method described in any one of claims 1 to 11, or implements the steps of the method described in any one of claims 12 to 16.

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