CN114375589B - Network parameter adjusting method and network management equipment - Google Patents

Abstract

Embodiments of the present application disclose a network parameter adjustment method and network management equipment, which relate to the field of communications. The method optimizes the performance of the cluster by adjusting the network parameters of each cell in the cluster. The method includes: determining status information of the first cell group; wherein the status information is used to indicate traffic statistics indicators of the first cell group, the first cell group includes at least two cells, and the performance of the at least two cells affects each other; according to the The status information of a cell group and the network parameter recommendation algorithm determine the target value of the first network parameter of each cell in the first cell group; the network parameter recommendation algorithm is used to describe the status information, network parameters and the first cell group. Regarding the correspondence between performance indicators, when the value of the first network parameter of each cell is the corresponding target value, the first performance indicator of the first cell group is the best.

Description

A network parameter adjustment method and network management device

Technical Field

The embodiments of the present application relate to the field of communications, and in particular to a network parameter adjustment method and a network management device.

Background Art

Adjustment of wireless network parameters will affect the performance of the wireless network. To improve network performance, wireless network parameters can be adjusted. After a parameter of a cell is adjusted, the performance of the cell changes. In addition, it may also affect the performance of the neighboring cells of the cell. The cell and the affected neighboring cells can be called a cluster, and the parameters that affect the cluster are called cluster parameters.

Existing technologies can model a single cell and determine the cell's network parameters, such as intra-frequency handover parameters, inter-frequency handover parameters, and cell system parameters, based on the cell's coverage characteristics and the cell's performance optimization goals. The network side can configure the cell based on these parameters to improve cell performance.

This method can only optimize the performance of a cell in the cluster and may affect the performance of neighboring cells. For example, after the intra-frequency switching parameters of the cell are adjusted, the user rate of the cell is improved, but the user rate of the neighboring cell is reduced.

Summary of the invention

The embodiments of the present application provide a network parameter adjustment method and a network management device, which optimize the performance of a cluster by adjusting the network parameters of each cell in the cluster.

In a first aspect, a network parameter adjustment method is provided, the method comprising: a network management device may determine status information of a first cell group; wherein the status information is used to indicate a statistics indicator of the first cell group, the first cell group includes at least two cells, and the performance of the at least two cells affects each other. The network management device may also recommend optimized network parameters, and the performance of the first cell group may be improved by configuring the cells included in the first cell group according to the optimized network parameters. Specifically, a target value of a first network parameter of each cell in the first cell group may be determined based on the status information of the first cell group and a network parameter recommendation algorithm; the network parameter recommendation algorithm is used to describe the correspondence between status information, network parameters, and performance indicators of the first cell group, and when the value of the first network parameter of each cell is the corresponding target value, the first performance indicator of the first cell group is optimal.

In the method provided in the embodiment of the present application, the network management device can use the state information of the first cell group as the input of the network parameter recommendation algorithm to determine the first performance indicator of the first cell group under different values of the first network parameter. Finally, the first network parameter value that makes the first performance indicator of the first cell group optimal is screened out, that is, the target value of the embodiment of the present application. The input of the network parameter recommendation algorithm is the state information of the cell group and the network parameters of each cell in the cell group, and the output is the performance indicator of the cell group. Keeping the state information of the cell group unchanged, the value of the first network parameter is different, and the level of the first performance indicator of the cell group is different. It can be seen that the embodiment of the present application is different from the prior art, and there is no need to be limited to a single cell for performance optimization and ignore the performance of the entire cell group. The method provided in the embodiment of the present application can optimize the performance of a cell group (for example, a cluster) by adjusting the network parameters.

In combination with the first aspect, in a first possible implementation manner of the first aspect, determining the status information of the first cell group includes: determining the status information of the first cell group according to a traffic statistics indicator of each of the at least two cells.

In the method provided in the embodiment of the present application, the status information of each cell in the cell group can be calculated to obtain the status information of the cell group, so as to combine the network parameter recommendation algorithm to predict the performance indicators of the first cell group when the network parameters take different values in a scenario where the status of the cell group remains unchanged.

In combination with the first aspect or the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, determining a target value of a first network parameter of each cell in the first cell group according to the status information of the first cell group and a network parameter recommendation algorithm, including: determining a first target value corresponding to the ith cell in the first cell group according to the network parameter recommendation algorithm; the values of the first network parameters of the remaining cells in the first cell group except the ith cell are default values, the value of the first network parameter of the ith cell is the first target value, and the value of the first network parameter of the ith cell is higher than the other candidate values. The first performance indicator of the first cell group label; i is an integer greater than or equal to 1; the second target value corresponding to the i+1th cell in the first cell group is determined according to the network parameter recommendation algorithm; the values of the first network parameters of the first i cells in the first cell group are the corresponding target values, the values of the first network parameters of the remaining cells in the first cell group except the first i cells are the default values, the value of the first network parameter of the i+1th cell is the second target value and the value of the first network parameter of the first i cells is the corresponding target value, the first performance indicator of the first cell group when the value of the first network parameter of the first cell group is higher than the other candidate values and the value of the first network parameter of the first i cells is the corresponding target value.

In the method provided in the embodiment of the present application, the target value of the network parameter of the cells in the cell group is determined one by one, and the value of the first network parameter of each cell in the cell group is integrated to obtain the best performance index. Different from the traditional traversal algorithm, it can reduce the computing load of the network management device.

In combination with the first aspect or the first or second possible implementation method of the first aspect, in a third possible implementation method of the first aspect, status information of at least one cell group is determined; status information of at least one cell group, at least one network parameter, and at least one performance indicator are learned to determine a network parameter recommendation algorithm.

In the method provided in the embodiment of the present application, the network management device can also use a machine learning model to learn the historical indicators of multiple cells and obtain a network parameter recommendation algorithm to predict the performance indicators of the cell group based on the status information and network parameters of the cell group.

In combination with the third possible implementation method of the first aspect, in a fourth possible implementation method of the first aspect, determining the status information of at least one cell group includes: for each cell group in the at least one cell group, determining the status information of the cell group according to the traffic statistics indicators of each cell in the cell group.

In the method provided in the embodiment of the present application, the traffic statistics indicators of each cell in the cell group can be calculated to obtain the status information of the cell group, which can be input into the machine learning model for learning to obtain a network parameter recommendation algorithm.

In combination with the first aspect or any one of the first to fourth possible implementations of the first aspect, in the fifth possible implementation of the first aspect, the first network parameter is any one of the following parameters: a cell system parameter, an intra-frequency cell switching threshold, an inter-frequency cell switching threshold, or a load balancing parameter.

The embodiments of the present application provide a specific implementation of network parameters, and the performance of a cell group can be changed by adjusting the values of the above network parameters.

In combination with the first aspect or any one of the first to fifth possible implementations of the first aspect, in the sixth possible implementation of the first aspect, at least two cells are same-frequency cells, or at least two cells are different-frequency cells, or at least two cells belong to the same sector, or at least two cells belong to the same single-frequency networking SFN area.

The embodiment of the present application provides a specific implementation of a cell group, wherein the cluster may include at least two same-frequency cells or at least two different-frequency cells, and the cluster may be a sector or a SFN area. The method provided in the embodiment of the present application improves the performance of the cluster by adjusting the network parameters of the cell.

In a second aspect, a network management device is disclosed, including: a parameter processing unit, used to determine status information of a first cell group; wherein the status information is used to indicate a statistics indicator of the first cell group, the first cell group includes at least two cells, and the performance of at least two cells affects each other; a parameter recommendation unit, used to determine a target value of a first network parameter of each cell in the first cell group according to the status information of the first cell group and a network parameter recommendation algorithm; the network parameter recommendation algorithm is used to describe the correspondence between the status information, network parameters and performance indicators of the first cell group, and when the value of the first network parameter of each cell is the corresponding target value, the first performance indicator of the first cell group is optimal.

In combination with the second aspect, in a first possible implementation manner of the second aspect, the parameter processing unit is specifically used to determine the state information of the first cell group according to the traffic statistics indicator of each cell in the at least two cells.

In combination with the second aspect or the first possible implementation manner of the second aspect, in the second possible implementation manner of the second aspect, the parameter recommendation unit is specifically used to determine, according to the network parameter recommendation algorithm, a first target value corresponding to the i-th cell in the first cell group; the values of the first network parameters of the remaining cells in the first cell group except the i-th cell are default values, the value of the first network parameter of the i-th cell is the first target value when the value is the first performance indicator of the first cell group, and the value of the first network parameter higher than the i-th cell is the other candidate values when the value is the first performance indicator of the first cell group; i is an integer greater than or equal to 1; determine, according to the network parameter recommendation algorithm, a second target value corresponding to the i+1-th cell in the first cell group; the values of the first network parameters of the first i cells in the first cell group are the corresponding target values, the values of the first network parameters of the remaining cells in the first cell group except the first i cells are default values, the value of the first network parameter of the i+1-th cell is the second target value and the value of the first network parameter of the first i cells is the corresponding target value when the first performance indicator of the first cell group, and the value of the first network parameter higher than the i+1-th cell is the other candidate values and the value of the first network parameter of the first i cells is the corresponding target value.

In combination with the second aspect or the first or second possible implementation of the second aspect, in a third possible implementation of the second aspect, the parameter recommendation module is also used to determine status information of at least one cell group; learn the status information of at least one cell group, at least one network parameter, and at least one performance indicator, and determine a network parameter recommendation algorithm.

In combination with the second aspect or the first to third possible implementation methods of the second aspect, in the fourth possible implementation method of the second aspect, the parameter recommendation unit is specifically used to determine, for each cell group in at least one cell group, the status information of the cell group based on the traffic statistics indicators of each cell in the cell group.

In combination with the second aspect or the first to fourth possible implementations of the second aspect, in the fifth possible implementation of the second aspect, the first network parameter is any one of the following parameters: a cell system parameter, an intra-frequency cell switching threshold, an inter-frequency cell switching threshold, or a load balancing parameter.

In combination with the second aspect or the first to fifth possible implementation methods of the second aspect, in the sixth possible implementation method of the second aspect, at least two cells are same-frequency cells, or at least two cells are different-frequency cells, or at least two cells belong to the same sector, or at least two cells belong to the same single-frequency networking SFN area.

In a third aspect, an embodiment of the present application provides a communication network management device, which can implement the method in the first aspect or any possible implementation of the first aspect. The network management device includes corresponding units or components for executing the above method. The units included in the network management device can be implemented by software and/or hardware. The network management device can be a network device, or a chip, a chip system, or a processor that can support the network device to implement the above method.

In a fourth aspect, the present application provides a communication network management device, comprising: a processor, the processor is coupled to a memory, the memory is used to store programs or instructions, when the program or instructions are executed by the processor, the network management device implements the method described in the above-mentioned first aspect, or any possible implementation of the first aspect.

In a fifth aspect, the present application provides a storage medium having a computer program or instructions stored thereon, which, when executed, enables a computer to execute the method described in the above-mentioned first aspect, or any possible implementation of the first aspect.

In a sixth aspect, an embodiment of the present application provides a communication system, comprising: the network management device and network device described in the second aspect above; or, the network management device, network device and terminal device described in the second aspect above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a communication system provided in an embodiment of the present application;

FIG2 is a structural block diagram of a network management device provided in an embodiment of the present application;

FIG3 is a schematic diagram of a cluster provided in an embodiment of the present application;

FIG4 is a schematic diagram of same-frequency switching provided in an embodiment of the present application;

FIG5 is a schematic diagram of inter-frequency switching provided in an embodiment of the present application;

FIG6 is a flow chart of a network parameter adjustment method provided in an embodiment of the present application;

FIG7 is another structural block diagram of a network management device provided in an embodiment of the present application;

FIG8 is another structural block diagram of the network management device provided in an embodiment of the present application.

DETAILED DESCRIPTION

FIG1 is a schematic diagram of a communication system to which the technical solution provided by the present application is applicable, and the communication system may include multiple network devices (only the network device 100 is shown), multiple terminal devices (only the terminal device 201 and the terminal device 202 are shown in the figure), and a network management device 300. FIG1 is only a schematic diagram and does not constitute a limitation on the applicable scenarios of the technical solution provided by the present application.

Among them, the network device and the terminal device can communicate through a cellular link (Uu link), for example, the network device 100 and the terminal device 201 can communicate, and the terminal devices can communicate through a sidelink link, for example, the terminal device 201 and the terminal device 202 can communicate, and the sidelink link can include D2D communication, V2X communication and machine type communication (MTC), etc.

The network device 100 can be any device with wireless transceiver function. Including but not limited to: evolved base station (NodeB or eNB or e-NodeB, evolutional Node B) in LTE, base station (gNodeB or gNB) or transceiver point (transmission receiving point/transmission reception point, TRP) in NR, base station of subsequent evolution of 3GPP, access node in WiFi system, wireless relay node, wireless backhaul node, etc. The base station can be: macro base station, micro base station, micro-micro base station, small station, relay station, or balloon station, etc. Multiple base stations can support the network of the same technology mentioned above, or they can support the network of different technologies mentioned above. The base station can include one or more co-station or non-co-station TRPs. The network device can also be a wireless controller, a centralized unit (CU), and/or a distributed unit (DU) in a cloud radio access network (CRAN) scenario. The network device can also be a server, a wearable device, or a vehicle-mounted device, etc. The following is an example of a network device being a base station. The multiple network devices may be base stations of the same type or different types. The base station may communicate with the terminal device or communicate with the terminal device through a relay station. The terminal device may communicate with multiple base stations of different technologies. For example, the terminal device may communicate with a base station supporting an LTE network or a base station supporting a 5G network, and may also support dual connection with a base station of an LTE network and a base station of a 5G network.

The terminal device (such as terminal device 201 or terminal device 202) is a device with wireless transceiver function, which can be deployed on land, including indoors or outdoors, handheld, wearable or vehicle-mounted; it can also be deployed on the water surface (such as ships, etc.); it can also be deployed in the air (such as airplanes, balloons and satellites, etc.). The terminal can be a mobile phone, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a vehicle-mounted terminal device, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, a wearable terminal device, etc. The embodiments of the present application do not limit the application scenarios. The terminal may also be sometimes referred to as terminal equipment, user equipment (UE), access terminal equipment, vehicle-mounted terminal, industrial control terminal, UE unit, UE station, mobile station, mobile station, remote station, remote terminal equipment, mobile equipment, UE terminal equipment, terminal equipment, wireless communication equipment, UE agent or UE device, etc. The terminal may also be fixed or mobile. The terminal equipment of the present application may also be a vehicle-mounted module, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or vehicle-mounted unit built into the vehicle as one or more components or units, and the vehicle may implement the method of the present application through the built-in vehicle-mounted module, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or vehicle-mounted unit.

The network management device 300 is used to manage the network device 100 , for example, to send network parameters to the network device 100 so that the network device 100 configures a cell according to the network parameters sent by the network management device 300 .

In the prior art, the network management device 300 can send the network parameters that need to be adjusted for a cell to the network device 100 according to the coverage characteristics of the cell and the optimization target of the cell performance. The network device 100 can configure the cell according to these parameters to improve the cell performance.

The embodiment of the present application provides a network parameter adjustment method, wherein the network management device determines the state information of the first cell group; wherein the state information is used to indicate the speech statistics index of the first cell group. In addition, the first cell group (for example, the first cell group may be a cluster) includes at least two cells, and the performance of the at least two cells affects each other. The network device may also use the state information of the first cell group as the input of the network parameter recommendation algorithm to determine the first performance index of the first cell group under different values of the first network parameter. Finally, the first network parameter value that makes the first performance index of the first cell group the best is screened out, that is, the target value described in the embodiment of the present application. Among them, the network parameter recommendation algorithm is used to describe the correspondence between the state information of the cell group, the network parameters and the performance index of the cell group. The input of the network parameter recommendation algorithm is the state information of the cell group and the network parameters of each cell in the cell group, and the output is the performance index of the cell group. Keeping the state information of the cell group unchanged, the value of the first network parameter is different, and the first performance index of the cell group is different. It can be seen that the embodiment of the present application is different from the prior art, and there is no need to be limited to a single cell for performance optimization and ignore the performance of the entire cell group. The method provided in the embodiment of the present application can optimize the performance of the cell group (for example, cluster) by adjusting the network parameters.

FIG2 is a schematic diagram of the hardware structure of a network management device provided in an embodiment of the present application. The network management device may be the network management device described in an embodiment of the present application. Referring to FIG2 , the network management device includes a processor 201, a memory 202, and at least one network interface (FIG. 2 is only exemplary and takes the network interface 203 as an example for explanation). The processor 201, the memory 202, and the network interface 203 are interconnected.

The processor 201 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application.

The network interface 203 is an interface of the network management device, and is used to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

The memory 202 may be a read-only memory (ROM) or other types of static data centers that can store static information and instructions, a random access memory (RAM) or other types of dynamic data centers that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic data centers, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor through a communication line. The memory may also be integrated with the processor.

The memory 202 is used to store computer-executable instructions for executing the solution of the present application, and the execution is controlled by the processor 201. The processor 201 is used to execute the computer-executable instructions stored in the memory 202, thereby realizing the intention processing method provided in the following embodiments of the present application.

Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application code, which is not specifically limited in the embodiments of the present application.

In a specific implementation, as an embodiment, the processor 201 may include one or more CPUs, such as CPU0 and CPU1 in FIG. 2 .

In a specific implementation, as an embodiment, the network management device may include multiple processors, such as processor 201 and processor 204 in FIG. 2. Each of these processors may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).

In a specific implementation, as an embodiment, the network management device may further include an output device 204 and an input device 205. The output device 204 communicates with the processor 201 and may display information in a variety of ways. For example, the output device 204 may be a liquid crystal display (LCD), a light emitting diode (LED) display device, a cathode ray tube (CRT) display device, or a projector. The input device 205 communicates with the processor 201 and may receive user input in a variety of ways. For example, the input device 205 may be a mouse, a keyboard, a touch screen device, or a sensor device.

The above network management device can be a general device or a special device. In a specific implementation, the network management device can be a desktop computer, a network management device, an embedded device or other devices with a similar structure as shown in Figure 2. The embodiment of the present application does not limit the type of network management device.

First, the terms involved in the embodiments of the present application are explained:

(1) Community Group

The cell group includes at least two cells, and the performance of the cells therein affects each other. It is understandable that if the performance of a cell in the cell group changes, the performance of other cells in the cell group will also change. The cell group in the embodiment of the present application can be a cluster, a single frequency network (SFN) area, a sector, etc. The cells in the cell group can be co-frequency cells or hetero-frequency cells.

The cluster can be determined according to the adjustment of the network parameters. For example, referring to FIG3, the network parameter P of cell A is adjusted, the performance of cell A changes, and the performance of cells C and D are affected, while the performance of cells B and E are not affected. Then, for the network parameter P, there is a cluster including cells A, C and D.

Specifically, the sector may be determined according to the engineering parameters corresponding to the cell. For example, if the longitude and latitude of the sites where different cells are located are the same, and their respective azimuths are also the same, then the cells belong to the same sector.

(2) Status information

In the embodiment of the present application, the state information is used to indicate a traffic statistics indicator, wherein the traffic statistics indicator may be a counter (statistical) indicator, for example, the state information of a cell is used to indicate a counter indicator of the cell, such as the number of users, flow, coverage, interference, etc. of the cell.

The status information of the cell group is used to indicate the counter index of the cell group, and the counter index of the cell group can be determined according to the counter index of each cell in the cell group. Specifically, the counter index of each cell can be calculated using a specified algorithm to obtain the counter index of the cell group. For example, the counter index of each cell in the cell group can be summed to obtain the counter index of the cell group.

For example, the cluster includes cell A, cell C and cell D, the number of users in cell A is 500, the number of users in cell C is 380, and the number of users in cell D is 420, then the number of users in the cluster is 500+380+420=1300. Alternatively, the flow rate of cell A is _X1 , the flow rate of cell C is _X2 , the flow rate of cell D is _X3 , and the flow rate of the cluster is a* _X1 +c* _X2 +d* _X3 , where a, c, d are the weight coefficients of A, cell C and cell D respectively.

(3) Network parameters

The network parameter is a configuration parameter of a cell, and adjusting the value of the configuration parameter of a cell can have an impact on the performance of the cell. In the embodiment of the present application, the network parameter can be a configuration parameter among the configuration parameters of the cell that affects the performance of the cell and other cells in the cell group. It can be understood that after adjusting the value of a certain network parameter of the cell, the performance of the cell changes, and at the same time, the performance of other cells in the cell group also changes, and it can be considered that the performance of the cell group has changed. In the embodiment of the present application, the network parameter impact can be referred to as a cell coordination class parameter, a group-level network parameter, etc., and the embodiment of the present application does not limit the name of the network parameter.

For example, the network parameter may be a cell system parameter, an intra-frequency cell switching threshold, an inter-frequency cell switching threshold, or a load balancing parameter.

Specifically, the cell system parameters are parameters used to control the cell system performance and KPI, which may be cell scheduling parameters, power control parameters, trigger threshold parameters, and the like.

The intra-frequency cell switching threshold is used to control the switching of terminal devices between intra-frequency cells. For example, referring to Figure 4, the terminal device resides in cell A. When the signal reception quality measured by the terminal device meets the intra-frequency cell switching threshold, the terminal device switches to the intra-frequency cell B of cell A. The intra-frequency cell refers to a cell with the same central frequency.

It should be noted that the switching of terminal devices between co-frequency cells may affect the performance of co-frequency cells. Therefore, adjusting the co-frequency cell switching threshold may affect the performance of co-frequency cells. In a possible implementation, the cell group includes co-frequency cells. After adjusting the co-frequency switching threshold, the performance of the cell group may change. In the embodiment of the present application, it is considered that the co-frequency cell switching threshold is a network parameter that affects the performance of the cell group.

Optionally, the intra-frequency cell switching threshold may be a triggering threshold of an A1 event. When the triggering threshold of an A1 event is met, the A1 event is triggered, and the terminal device residing in the cell switches to another cell with the same frequency as the cell.

The inter-frequency cell switching threshold is used to control the switching of terminal devices between inter-frequency cells. For example, referring to Figure 5, the terminal device resides in cell A. When the signal reception quality measured by the terminal device meets the inter-frequency cell switching threshold, the terminal device switches to the inter-frequency cell C of cell A. The inter-frequency cell refers to a cell with a different central frequency.

It should be noted that the switching of terminal devices between inter-frequency cells may affect the performance of the inter-frequency cells. Therefore, adjusting the inter-frequency cell switching threshold may affect the performance of the inter-frequency cells. In a possible implementation, the cell group includes inter-frequency cells. After adjusting the inter-frequency switching threshold, the performance of the cell group may change. In the embodiment of the present application, it is considered that the inter-frequency cell switching threshold is a network parameter that affects the performance of the cell group.

Optionally, the inter-frequency cell switching threshold may be a triggering threshold of an A3 event. When the triggering threshold of an A3 event is met, the A3 event is triggered, and a terminal device residing in the cell switches to another cell with an inter-frequency frequency with the cell.

The mobility load balancing (MLB) parameter is used to adjust the load of each cell in the same cell group. For example, by configuring the MLB parameter, the load of each cell in the same sector can be balanced to avoid excessive load in some cells, which affects the performance of the entire sector.

Optionally, the network parameters may be radio frequency (RF) parameters of the cell. By adjusting the RF parameters of the cell, the performance of the cell changes, thereby affecting the performance of certain neighboring cells. Therefore, the RF parameters of the cell are considered to be network parameters that affect the cell group. By adjusting the RF parameters of a cell in the cell group, the performance of the cell group can be changed. Among them, the RF parameters of the cell may be antenna engineering parameters of a site that provides coverage for the cell, such as the azimuth angle and tilt angle of the antenna.

The network parameter can also be a cell reselection parameter. The cell reselection parameter is used to enable the terminal device to re-access in the idle state. For example, when the terminal device is disconnected from cell A and is in the idle state, the terminal device can access cell D according to the cell reselection parameter. When the terminal device is connected to the port of the cell and performs cell reselection, the performance of some neighboring cells may be affected. Therefore, the cell reselection parameter is considered to be a network parameter that affects the cell group. The performance of the cell group can be changed through the cell reselection parameter.

(4) Performance indicators of cell groups

In the embodiment of the present application, the performance indicators of the cell group are used to describe the performance of the cell group. For example, the performance indicators of the cell group can be the average user rate of the cell, the proportion of cell edge rates less than 5M, the network key performance indicator (Key Performance Indicator, KPI), and the VoLTE (Voice over LTE) call drop rate. Among them, the network KPI can be the call drop rate, access success rate, etc.

(5) Network parameter recommendation algorithm

In an embodiment of the present application, a network parameter recommendation algorithm is used to describe the correspondence between network parameters, status information of a cell group, and performance indicators of a cell group. The network parameter recommendation algorithm can be expressed as (r1, r2) = (s, a), where s represents the cell group status information, a represents a certain network parameter of each cell in the cell group (for example, the first network parameter described in an embodiment of the present application), and r1 and r2 represent the performance indicators of the cell group. It can be understood that the input of the network parameter recommendation algorithm is the status information of the cell group and a certain network parameter of each cell in the cell group, and the output of the network parameter recommendation algorithm is the performance indicator of the cell group. The status information of the cell group can be kept unchanged, and when the value of the network parameter of the cell changes, the performance indicators of the cell group are different.

In a specific implementation, the network management device can obtain the status information of multiple different cell groups, the network parameters of the cells, and the performance indicators, and learn the status information of multiple different cell groups, the network parameters of the cells, and the performance indicators according to the machine learning model to obtain a network parameter recommendation algorithm. The machine learning model can be a deep learning model, such as a support vector machine (SVM) model, a random forest (RF) model, etc.

In one possible implementation, the network management device may determine status information of at least one cell group;

The state information of the at least one cell group, at least one network parameter, and at least one performance indicator are learned to determine the network parameter recommendation algorithm. The determining of the state information of the at least one cell group includes: for each cell group in the at least one cell group, determining the state information of the cell group according to the traffic statistics indicator of each cell in the cell group.

For example, the network management device may determine the network parameter recommendation algorithm (also referred to as the network parameter recommendation model) through the following three steps:

S1. Determine multiple cell groups.

Specifically, for a certain cell, a cell group including the cell is determined. For example, the same-frequency cells in the neighboring cells of the present cell can be determined, and a cell group can also be determined, including the present cell and the first N same-frequency cells with a large number of switching times. Alternatively, the different-frequency cells in the neighboring cells of the present cell can be determined, and a cell group can also be determined, including the present cell and the first N different-frequency cells with a large number of switching times. Alternatively, the cells within a sector constitute a cell group. Wherein, N is a configurable value.

S2. Obtain statistical indicators of the cell, and determine status information of each cell group according to the statistical indicators of the cell.

In a specific implementation, the network management device first obtains statistical indicators of the cell from the network device (e.g., base station), and the statistical indicators include cell status information, network parameters, and performance indicators, etc. The status information of each cell in the cell group can also be calculated to obtain the status information of the cell group.

In a possible implementation, the network management device collects and analyzes call statistics data, configuration data, and operation logs, and can also process the collected data based on time granularity, including data segmentation and splicing. For example, the processed data is the call statistics index within 15 minutes. The processed data includes status information, performance indicators, network parameters, etc.

Secondly, the state information of the cell group is determined based on the processed data. Specifically, the state information of the cell can be an indicator of an accumulation type, that is, the state information of the cell group can be obtained by summing the indicators of each cell in the cell group. The state information of the cell can also be an indicator of a calculation type, that is, the indicator of each cell in the cell group is first calculated (for example, the numerator and the denominator are added), and the state information of the cell group is obtained according to the result of the calculation.

The following uses the cell group cluster as an example to explain how to obtain the status information of the cell group based on the statistical indicators of the cell:

(1) Status information is an indicator of cumulative type: The indicators of each cell in the cluster are added together to obtain the status information of the cluster. For example, the traffic of each cell in the cluster is added together to obtain the cluster traffic, the number of users of each cell in the cluster is added together to obtain the number of users of the cluster, and the scheduling time of each cell in the cluster is added together to obtain the scheduling time of the cluster.

(2) Indicators of the calculation type based on status information: For the indicators of each cell in the cluster, the numerator and denominator of the parameter are added together before calculating the status information of the cluster. For example, the parameter FULLBUFFER rate involves statistical period traffic and scheduling duration, where the statistical period traffic is the numerator and the scheduling duration is the denominator. The statistical period traffic corresponding to each cell can be added together to obtain the total statistical period traffic, the scheduling duration corresponding to each cell can be added together to obtain the total scheduling duration, and the total statistical period traffic can be divided by the total scheduling duration to obtain the FULLBUFFER rate of the cluster.

It should be noted that the status information of the cell group can also be marked, for example, the status information of the same-frequency cluster is marked as NN.IntraC XXX, the status information of the different-frequency cluster is marked as NN.InterC.XXX, and the status information of the sector cluster is marked as NN.SectorC XXX. Among them, the same-frequency cluster means that the cells in the cluster are all same-frequency cells, the different-frequency cluster means that the cells in the cluster are all different-frequency cells, the sector cluster means that the cells in the cluster belong to the same sector, and XXX represents the parameters of the status information class.

Table 1 shows some statistical indicators and calculation formulas for operating these indicators, which are used to obtain the status information of the cell group:

Table 1

Referring to Table 1, _Ai is the reference signal receiving power (RSRP) on the physical uplink shared channel (PUSCH) of the i-th cell in the cell group within a sampling period. In which, a single terminal device is used as a sampling point, and RSRP is a value measured by a terminal device. _Bi is the number of sampling times in the i-th cell in the cell group within a sampling period.

_Ci is the path loss value of the UE residing in the i-th cell in the cell group, and _Dj is the j-th path loss value greater than a preset threshold. In a possible implementation, the path loss values of the terminal devices in the radio resource control (RFC) connection state in the cell group can be sampled periodically.

E _i is the number of users in the i-th cell in the cell group within a sampling period.

F _i is the data size of a service data unit (SDU) successfully sent by the i-th cell in a cell group within a sampling period.

_Gi is the accumulated value of the channel quality indicator (CQI) values reported by each UE residing in the i-th cell in the cell group; Hi is the accumulated value of the number of times each UE in the i-th cell in the cell group reports CQI.

_Gi is the noise power measured on the i-th cell in the cell group within a sampling period. For example, the noise power may be measured on an uplink physical resource block (PRB).

_Li is the number of available downlink PRBs of the i-th cell in the cell group; _Ki is the number of downlink PRBs actually used by the i-th cell in the cell group.

P _i is the number of available uplink PRBs of the i-th cell in the cell group; S _i is the number of downlink PRBs actually used by the i-th cell in the cell group.

_Wi is the sum of the data sizes of the SDUs sent by the ith cell in the cell group; the number of available uplink PRBs; _Vi is the sum of the effective durations of the SDUs sent by the ith cell in the cell group. It should be noted that the duration scheduled by the base station but not actually sending data is not calculated.

_Qi is the cumulative number of times the throughput is less than 5 Mbps when the SDU is sent on the i-th cell in the cell group; the number of available uplink PRBs; _Ti is the total number of times the throughput is counted when the SDU is sent on the i-th cell in the cell group.

S3. Learn the network parameters, status information of the cell group, and performance indicators of the cell group, and determine a network parameter recommendation algorithm.

Specifically, the machine learning model learns network parameters, state information of cell groups, and performance indicators of cell groups to obtain a network parameter recommendation algorithm. The network parameter recommendation algorithm can be described as: r = f(s, a).

Where r is the performance index of the cell group. This algorithm supports modeling and prediction of multiple targets at the same time. It can be understood that the output of the network parameter recommendation algorithm can be multiple performance indicators. For example, (r ₁ , r ₂ ) = f(s, a).

s is the status information of the cell group, which may include multiple indicators, such as the number of users, traffic, CQI, etc.

a is a network parameter, which can be a cell system parameter, intra-frequency handover, inter-frequency handover, sector load balancing, etc. The network parameter can also include multiple values. For example, assuming that the cluster includes 5 cells, a can include 5 values, which are the intra-frequency handover parameters P1, P2, P3, P4, and P5 of the 5 cells.

f represents the model relationship between the network parameter a, the state information s of the cell group and the performance indicator r of the cell group.

Parameter a can be or can be a system parameter that affects this cell.

The embodiment of the present application provides a network parameter adjustment method, which is applied to the network management device in FIG. 1 , as shown in FIG. 6 , and the method includes the following steps:

601. Determine status information of a first cell group; wherein the status information is used to indicate a traffic statistics indicator of the first cell group, the first cell group includes at least two cells, and performance of the at least two cells affects each other.

In a specific implementation, the network management device can determine the status information of the first cell group according to the traffic statistics index of each of the at least two cells included in the first cell group. The traffic statistics index of the cell can be a counter index of the cell, such as the number of users and traffic of the cell.

In a possible implementation, the state information of the first cell group may be obtained by calculating the traffic statistics indicators of each cell in the first cell group. For example, the traffic statistics indicators of each cell in the first cell group are summed to obtain the state information of the first cell group.

It should be noted that the cells in the first cell group may be same-frequency cells or different-frequency cells, the first cell group is a sector, or the first cell group is an SFN area. It is understandable that at least two cells in the first cell group are same-frequency cells or different-frequency cells, or the at least two cells are different-frequency cells, or the at least two cells belong to the same sector, or the at least two cells belong to the same SFN area.

602. Determine a target value of a first network parameter for each cell in the first cell group based on the status information of the first cell group and a network parameter recommendation algorithm; the network parameter recommendation algorithm is used to describe the correspondence between the status information, network parameters and performance indicators of the first cell group. When the value of the first network parameter of each cell is the corresponding target value, the first performance indicator of the first cell group is optimal.

It should be noted that the first network parameter is any one of the following parameters: a cell system parameter, a same-frequency cell switching threshold, an inter-frequency cell switching threshold, or a load balancing parameter. It is understandable that the network parameter recommendation algorithm can determine the performance indicators of the cell group under different cell status information and network parameters. The value of the network parameter and the performance indicator of the cell group are different. By adjusting the value of the network parameter, the best performance indicator can be obtained.

In a specific implementation, the performance index r to be optimized is first determined, and then the state information of the first cell group and the network parameters to be adjusted are determined, for example, the first network parameters described in the embodiment of the present application. The network parameter recommendation algorithm can predict the performance index r that can be obtained by adjusting the network parameter a under a certain state information s. Therefore, the value of the first network parameter is different, and the first performance index is different. The value that makes the first performance index the best can be selected, that is, the target value described in the embodiment of the present application.

In the embodiment of the present application, it is necessary to comprehensively consider the values of the first network parameters of each cell in the cell group to obtain the best performance index. The target values of the first network parameters of each cell may be the same or different, and the embodiment of the present application does not limit this. For each cell in the first cell group, first determine the target value corresponding to one of the cells, and the values of the first network parameters of the remaining cells are default values. Subsequently, fix the value of the first network parameter of the cell, and adjust the value of the first network parameter of another cell. Iterate until a set of values that makes the cell group performance index optimal is screened out.

Specifically, the first target value corresponding to the i-th cell in the first cell group is determined according to the network parameter recommendation algorithm. For the i-th cell in the first cell group, all candidate values of the first network parameter can be traversed to obtain different first performance indicators. It is also possible to screen out the value that makes the first performance indicator optimal, that is, the target value corresponding to the i-th cell. It should be noted that the value of the first network parameter of the remaining cells in the first cell group except the i-th cell is the default value, the value of the first network parameter of the i-th cell is the first performance indicator of the first cell group when the value of the first network parameter of the i-th cell is the first target value, and the value of the first network parameter of the i-th cell is higher than the first performance indicator of the first cell group when the value of the first network parameter of the i-th cell is the other candidate values; i is an integer greater than or equal to 1.

The value of the first network parameter of the i-th cell can also be fixed at the corresponding target value, and the target values of the first network parameters of other cells can be continuously determined. For example, the second target value corresponding to the i+1-th cell in the first cell group is determined according to the network parameter recommendation algorithm; the values of the first network parameter of the first i cells in the first cell group are the corresponding target values, the values of the first network parameter of the remaining cells in the first cell group except the first i cells are the default values, the value of the first network parameter of the i+1-th cell is the second target value and the value of the first network parameter of the first i cells is the corresponding target value, and the first performance indicator of the first cell group is higher than the value of the first network parameter of the i+1-th cell is the remaining candidate value and the value of the first network parameter of the first i cells is the corresponding target value.

It should be noted that the default value of the first network parameter may be one of the candidate values of the first network parameter, and the default values of the first network parameters of different cells may be the same or different, which is not limited in this embodiment of the present application.

For example, the first cell group includes cell A, cell B and cell C. Candidate values of the first network parameter include x ₁ , x ₂ , x ₃ , x ₄ , x ₅ , where x ₃ is a default value. First, the first network parameter of cell A is x ₁ , x ₂ , x ₃ , x ₄ , x ₅ in sequence, the first network parameter of cell B is fixed to x ₃ , and the first network parameter of cell C is fixed to x ₃ . The state information s and network parameter a of the first cell group are obtained as follows: (x ₁ , x ₃ , x ₃ ), (x ₂ , x ₃ , x ₃ ), (x ₃ , x ₃ , x ₃ ), (x ₄ , x ₃ , x ₃ ), (x ₅ , x ₃ , x ₃ ), and the first performance indicators obtained in sequence are: _{r 1} , r ₂ , r ₃ , r ₄ , r ₅ . Among them, r ₂ is the largest, and the target value of the first network parameter of cell A is x ₂ .

The first network parameter of cell A is fixed to x ₂ , the first network parameter of cell C is fixed to x ₃ , the first network parameter of cell B is taken to be x ₁ , x ₂ , x ₃ , x ₄ , x ₅ in sequence, the state information s of the first cell group and the network parameter a=(x ₂ , x ₁ , x ₃ ), (x ₂ , x ₂ , x ₃ ), (x ₂ , x ₃ , x ₃ ), (x ₂ , x ₄ , x ₃ ), (x ₂ , x ₅ , x ₃ ), the first performance indicators obtained in sequence are: r ₆ , r ₇ , r ₈ , r ₉ , r ₁₀ . Among them, r ₉ is the largest, and r ₉ is greater than r ₂ , then the target value of the first network parameter of cell B is x ₄ .

The first network parameter of cell A is fixed to x ₂ , the first network parameter of cell B is fixed to x ₄ , the first network parameter of cell C is fixed to x ₃ , the first network parameter of cell C is taken as x ₁ , x ₂ , x ₃ , x ₄ , x ₅ in sequence, the state information s of the first cell group and the network parameter a=(x ₂ , x ₄ , x ₁ ), (x ₂ , x ₄ , x ₂ ), (x ₂ , x ₄ , x ₃ ), (x ₂ , x ₄ , x ₄ ), (x ₂ , x ₄ , x ₅ ), the first performance indicators obtained in sequence are: _{r 11} , r ₁₂ , r ₁₃ , r ₁₄ , r ₁₅ . Among them, r ₁₅ is the largest, and r ₁₅ is greater than r ₉ and r ₂ , then the target value of the first network parameter of cell C is x ₅ .

When the first network parameter of cell A is x ₂ , the first network parameter of cell B is x ₄ , and the first network parameter of cell C is x ₅ , the first performance indicator of the first cell group is optimal, and the recommended first network parameters for the first cell group are x ₂ , x ₄ , and x ₅ .

Optionally, the network management device may send a target value of a first network parameter to a network device (eg, a base station), so that the network device may adjust the value of the first network parameter according to the target value of the first network parameter to optimize the performance of the first cell group.

In the case of dividing each functional module according to each function, FIG7 shows a possible structural diagram of the network management device involved in the above embodiment. The network management device shown in FIG7 can be the network management device described in the embodiment of the present application, or it can be a component in the network management device that implements the above method. As shown in FIG7, the network management device includes a parameter processing unit 701, a parameter recommendation unit 702, and a transceiver unit 703. The processing unit can be one or more processors, and the transceiver unit 703 can be a network interface.

The parameter processing unit 701 is used to obtain statistical indicators of the cell, for example, to support the network management device to execute step 601, and/or other processes used in the technology described in this document.

The parameter recommendation unit 702 is used to recommend optimized network parameters of cells to improve the performance of the cell group, for example, to support the network management device to execute step 601 and/or other processes for the technology described herein.

The transceiver unit 703 is used to support, for example, communication between the network management device and other devices, for example, to support the network management device to send a target value of a first network parameter to a network device, and/or other processes for the technology described herein.

In a possible implementation, the network management device shown in Fig. 7 may also be a chip applied to a network management device or in a network management device. The chip may be a system-on-a-chip (SOC).

The above transceiver unit 702 for receiving/sending may be a network interface of the network management device, and is used to receive signals from other network management devices.

Exemplarily, in the case of adopting an integrated unit, the structural schematic diagram of the network management device provided by the embodiment of the present application is shown in Figure 8. In Figure 8, the network management device includes: a processing module 801 and a communication module 802. The processing module 801 is used to control and manage the actions of the network management device, for example, to execute the steps performed by the above-mentioned parameter processing unit 701 and parameter recommendation unit 702, and/or other processes for executing the technology described herein. The communication module 802 is used to execute the steps performed by the above-mentioned transceiver unit 703, and supports the interaction between the network management device and other devices, such as interaction with other network devices. As shown in Figure 8, the network management device may also include a storage module 803, which is used to store program code and data of the network management device.

When the processing module 801 is a processor, the communication module 802 is a network interface, and the storage module 803 is a memory, the network management device is the network management device shown in FIG. 2 .

The present application provides a computer-readable storage medium, in which a computer instruction is stored, wherein the computer instruction instructs a network management device to execute the above-mentioned network parameter recommendation method, or the computer instruction is used to implement a functional unit included in the network management device.

The present application provides a computer program product, which includes computer instructions. The computer instructions instruct a network management device to execute the above-mentioned network parameter recommendation method, or the computer instructions are used to implement the functional units included in the network management device.

Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the database access device can be divided into different functional modules to complete all or part of the functions described above.

In the several embodiments provided in the present application, it should be understood that the disclosed database access device and method can be implemented in other ways. For example, the above-described database access device embodiment is only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the database access device or unit can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, disks, or optical disks.

The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims (14)

1. A method for adjusting network parameters, the method comprising:

determining state information of a first cell group; the state information is used for indicating a telephone system index of a first cell group, the first cell group comprises at least two cells, and the performances of the at least two cells are mutually influenced when a first network parameter is adjusted;

determining a target value of a first network parameter of each cell in the first cell group according to the state information of the first cell group and a network parameter recommendation algorithm; the network parameter recommendation algorithm is used for describing a corresponding relation among state information, network parameters and performance indexes of the first cell group, and when the value of the first network parameter of each cell is a corresponding target value, the first performance index of the first cell group is optimal; wherein the first network parameter is any one of the following parameters: cell system parameters, same frequency cell switching threshold, different frequency cell switching threshold or load balancing parameters.

2. The method of claim 1, wherein the determining the state information of the first group of cells comprises:

And determining the state information of the first cell group according to the voice system index of each cell in the at least two cells.

3. The method according to claim 1 or 2, wherein said determining a target value of a first network parameter for each cell in said first group of cells based on state information of said first group of cells and a network parameter recommendation algorithm comprises:

determining a first target value corresponding to an ith cell in the first cell group according to the network parameter recommendation algorithm; the value of the first network parameter of the rest cells except the ith cell in the first cell group is a default value, and the first performance index of the first cell group when the value of the first network parameter of the ith cell is the first target value is higher than the first performance index of the first cell group when the value of the first network parameter of the ith cell is the rest candidate values; the i is an integer greater than or equal to 1;

determining a second target value corresponding to the (i+1) th cell in the first cell group according to the network parameter recommendation algorithm; the value of the first network parameter of the first i cells in the first cell group is a corresponding target value, the values of the first network parameters of the remaining cells in the first cell group except the first i cells are default values, the value of the first network parameter of the i+1th cell is the second target value and the value of the first network parameter of the first i cells is the corresponding target value, the first performance index of the first cell group is higher than the first performance index of the first cell group when the value of the first network parameter of the i+1th cell is the remaining candidate values and the value of the first network parameter of the first i cells is the corresponding target value.

4. A method according to any one of claims 1-3, wherein the method further comprises:

determining state information of at least one cell group;

and learning the state information, the at least one network parameter and the at least one performance index of the at least one cell group, and determining the network parameter recommendation algorithm.

5. The method of claim 4, wherein said determining status information for at least one cell group comprises:

for each cell group in the at least one cell group, determining state information of the cell group according to a phone system index of each cell in the cell group.

6. The method according to any of claims 1-5, wherein the at least two cells are co-frequency cells, or wherein the at least two cells are inter-frequency cells, or wherein the at least two cells belong to the same sector, or wherein the at least two cells belong to the same single frequency networking SFN area.

7. A network management device, comprising:

a parameter processing unit, configured to determine state information of a first cell group; the state information is used for indicating a telephone system index of a first cell group, the first cell group comprises at least two cells, and the performances of the at least two cells are mutually influenced when a first network parameter is adjusted;

A parameter recommendation unit, configured to determine a target value of a first network parameter of each cell in the first cell group according to the state information of the first cell group and a network parameter recommendation algorithm; the network parameter recommendation algorithm is used for describing a corresponding relation among state information, network parameters and performance indexes of the first cell group, and when the value of the first network parameter of each cell is a corresponding target value, the first performance index of the first cell group is optimal; wherein the first network parameter is any one of the following parameters: cell system parameters, same frequency cell switching threshold, different frequency cell switching threshold or load balancing parameters.

8. The network management device of claim 7, wherein the parameter processing unit is specifically configured to determine the state information of the first cell group according to a session index of each of the at least two cells.

9. The network management device according to claim 7 or 8, wherein the parameter recommendation unit is specifically configured to determine a first target value corresponding to an i-th cell in the first cell group according to the network parameter recommendation algorithm; the value of the first network parameter of the rest cells except the ith cell in the first cell group is a default value, and the first performance index of the first cell group when the value of the first network parameter of the ith cell is the first target value is higher than the first performance index of the first cell group when the value of the first network parameter of the ith cell is the rest candidate values; the i is an integer greater than or equal to 1;

Determining a second target value corresponding to the (i+1) th cell in the first cell group according to the network parameter recommendation algorithm; the value of the first network parameter of the first i cells in the first cell group is a corresponding target value, the values of the first network parameters of the remaining cells in the first cell group except the first i cells are default values, the value of the first network parameter of the i+1th cell is the second target value and the value of the first network parameter of the first i cells is the corresponding target value, the first performance index of the first cell group is higher than the first performance index of the first cell group when the value of the first network parameter of the i+1th cell is the remaining candidate values and the value of the first network parameter of the first i cells is the corresponding target value.

10. The network management device according to any of claims 7-9, wherein the parameter recommendation module is further configured to determine status information of at least one cell group;

and learning the state information, the at least one network parameter and the at least one performance index of the at least one cell group, and determining the network parameter recommendation algorithm.

11. The network management device according to claim 10, wherein the parameter recommendation unit is specifically configured to determine, for each of the at least one cell group, state information of the cell group according to a session index of each of the cell groups.

12. The network management device according to any of claims 7-11, wherein the at least two cells are co-frequency cells, or wherein the at least two cells are inter-frequency cells, or wherein the at least two cells belong to the same sector, or wherein the at least two cells belong to the same single frequency networking SFN area.

13. A network management device, the network management device comprising a processor and a memory; the network management device performs the method of any of claims 1 to 6 when the processor executes the computer instructions in the memory.

14. A computer readable storage medium storing computer instructions which, when executed, cause a computer to perform the method of any one of claims 1 to 6.