CN112135309A - Network optimization method and device - Google Patents

Network optimization method and device Download PDF

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CN112135309A
CN112135309A CN202011137019.2A CN202011137019A CN112135309A CN 112135309 A CN112135309 A CN 112135309A CN 202011137019 A CN202011137019 A CN 202011137019A CN 112135309 A CN112135309 A CN 112135309A
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target cell
sampling time
sampling
time slot
transmission
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CN112135309B (en
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金雨超
李�一
郭景赞
乔自知
李德屹
郭省力
李京辉
王静云
程新洲
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China United Network Communications Group Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The embodiment of the invention provides a network optimization method and device, relates to the technical field of communication, and aims to determine whether the application of an MU-MIMO transmission mode in a target cell is effective or not so as to improve the efficiency of data transmission in a network. The method comprises the following steps: acquiring sampling data of a plurality of sampling time slots corresponding to a target cell, wherein the sampling data of one sampling time slot comprises the number of PRBs occupied by at least one user group, the number of transmission layers occupied by the at least one user group and the number of PRBs occupied by the at least one user group on a j-layer transmission layer; determining the number of MU-MIMO paired layers corresponding to the target cell according to the sampling data of each sampling time slot in the plurality of sampling time slots; and determining to perform network optimization on the target cell under the condition that the number of MU-MIMO paired layers corresponding to the target cell is less than or equal to the threshold of the number of MU-MIMO paired layers.

Description

Network optimization method and device
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a network optimization method and device.
Background
Currently, the network load condition of a certain cell may be determined by using Physical Resource Block (PRB) utilization. Specifically, the number of occupied PRBs of the cell at a certain time may be obtained, where the number of occupied PRBs is used to indicate how much frequency domain resources the cell occupies, and the PRB utilization of the cell is a ratio of the number of occupied PRBs to the number of available PRBs of the cell (for a cell or a network with a certain fixed bandwidth, the number of available PRBs is fixed). When the PRB utilization of the cell is greater than a certain threshold, it may be determined that the network load of the cell may be higher.
However, in the above method, it may not be accurate enough to consider only the influence of frequency domain resources on the network load of the cell, or to determine the network load condition of the cell only according to the PRB utilization.
Disclosure of Invention
Embodiments of the present invention provide a network optimization method and apparatus, which can determine whether an application of a multi-user-multiple-input-multiple-output (MU-MIMO) transmission mode in a target cell is valid, so as to improve efficiency of data transmission in a network.
In a first aspect, an embodiment of the present invention provides a network optimization method, including: acquiring sampling data of a plurality of sampling time slots corresponding to a target cell, wherein the sampling data of one sampling time slot comprises the number of PRBs occupied by at least one user group, the number of transmission layers occupied by the at least one user group and the number of PRBs occupied by the at least one user group on a j-layer transmission layer, j is an integer greater than or equal to 2, and one user group comprises at least two User Equipments (UE); determining the number of MU-MIMO paired layers corresponding to the target cell according to the sampling data of each sampling time slot in the plurality of sampling time slots, wherein the number of MU-MIMO paired layers corresponding to the target cell is used for representing the number of UE (user equipment) which can perform data transmission on the same time-frequency resource of the target cell; and determining to perform network optimization on the target cell under the condition that the number of MU-MIMO paired layers corresponding to the target cell is less than or equal to the threshold of the number of MU-MIMO paired layers.
In a second aspect, an embodiment of the present invention provides a network optimization apparatus, including: the device comprises an acquisition module and a determination module; the acquiring module is configured to acquire sample data of multiple sample slots corresponding to a target cell, where the sample data of one sample slot includes the number of PRBs occupied by at least one user group, the number of transmission layers occupied by the at least one user group, and the number of PRBs occupied by the at least one user group on a j-layer transmission layer, j is an integer greater than or equal to 2, and one user group includes at least two user equipment UE; the determining module is configured to determine, according to the sampling data of each sampling time slot in the multiple sampling time slots, the number of MU-MIMO pairing layers corresponding to the target cell, where the number of MU-MIMO pairing layers corresponding to the target cell is used to represent the number of UEs capable of performing data transmission on the same time-frequency resource by the target cell; the determining module is further configured to determine to perform network optimization on the target cell when the number of MU-MIMO paired layers corresponding to the target cell is less than or equal to the MU-MIMO paired layer number threshold.
In a third aspect, an embodiment of the present invention provides another network optimization apparatus, including: a processor, a memory, a bus, and a communication interface; the memory is used for storing computer-executable instructions, the processor is connected with the memory through a bus, and when the network optimization device runs, the processor executes the computer-executable instructions stored in the memory, so that the network optimization device executes the network optimization method provided by the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which includes a computer program, when the computer program runs on a computer, the computer is caused to execute a network optimization method provided in the first aspect.
In a fifth aspect, an embodiment of the present invention provides a computer program product containing instructions, which, when run on a computer, causes the computer to execute the network optimization method of the first aspect and any one of the implementations thereof.
The network optimization method and device provided by the embodiment of the invention have the advantages that the network optimization device obtains the sampling data of a plurality of sampling time slots corresponding to a target cell, wherein the sampling data of one sampling time slot comprises the number of PRBs (physical resource blocks) occupied by at least one user group, the number of transmission layers occupied by the at least one user group and the number of transmission layers occupied by the at least one user group; then, the network optimization device determines the number of MU-MIMO paired layers corresponding to the target cell according to the sampling data of each sampling time slot in the plurality of sampling time slots, wherein the number of MU-MIMO paired layers corresponding to the target cell is used for representing the number of UE (user equipment) which can perform data transmission on the same time-frequency resource of the target cell; and determining to perform network optimization on the target cell under the condition that the MU-MIMO pairing layer number corresponding to the target cell is less than or equal to the MU-MIMO pairing layer number threshold. In the embodiment of the invention, the network optimization device can determine the number of MU-MIMO paired layers corresponding to the target cell according to the occupation situation of the two resources based on the frequency domain resource occupation situation of the target cell and the space domain resource occupation situation of the target cell, and then determine whether the network optimization needs to be carried out on the target cell, and can determine whether the application of the MU-MIMO transmission mode in the target cell is effective, thereby improving the efficiency of data transmission in the network.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a hardware schematic diagram of a server according to an embodiment of the present invention;
fig. 2 is a first schematic diagram illustrating a network optimization method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of data transmission corresponding to sampling data according to an embodiment of the present invention;
fig. 4 is a second schematic diagram of a network optimization method according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a maximum value of the number of transmission layers corresponding to each sample time slot in multiple sample time slots according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an available number of transmission layers corresponding to each sample slot in multiple sample slots according to an embodiment of the present invention;
fig. 7 is a first schematic structural diagram of a network optimization apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a network optimization apparatus according to an embodiment of the present invention.
Detailed Description
The network optimization method and apparatus provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "e.g.," an embodiment of the present invention is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
The term "and/or" as used herein includes the use of either or both of the two methods.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
Based on the problems existing in the background art, embodiments of the present invention provide a network optimization method and apparatus, where a network optimization apparatus obtains sample data of multiple sample time slots corresponding to a target cell, where the sample data of one sample time slot includes the number of PRBs occupied by at least one user group, the number of transmission layers occupied by the at least one user group, and the number of transmission layers occupied by the at least one user group; then, the network optimization device determines the number of MU-MIMO paired layers corresponding to the target cell according to the sampling data of each sampling time slot in the plurality of sampling time slots, wherein the number of MU-MIMO paired layers corresponding to the target cell is used for representing the number of UE (user equipment) which can perform data transmission on the same time-frequency resource of the target cell; and determining to perform network optimization on the target cell under the condition that the MU-MIMO pairing layer number corresponding to the target cell is less than or equal to the MU-MIMO pairing layer number threshold. In the embodiment of the invention, the network optimization device can determine the number of MU-MIMO paired layers corresponding to the target cell according to the occupation situation of the two resources based on the frequency domain resource occupation situation of the target cell and the space domain resource occupation situation of the target cell, and then determine whether the network optimization needs to be carried out on the target cell, and can determine whether the application of the MU-MIMO transmission mode in the target cell is effective, thereby improving the efficiency of data transmission in the network.
An embodiment of the present invention provides a network optimization device, which may be a server, and fig. 1 is a hardware structure diagram of a server for executing the network optimization method provided in the embodiment of the present invention. As shown in fig. 1, the server 10 includes a processor 101, a memory 102, a network interface 103, and the like.
The processor 101 is a core component of the server 10, and the processor 101 is configured to run an operating system of the server 10 and application programs (including a system application program and a third-party application program) on the server 10, so as to implement the network optimization method performed by the server 10.
In this embodiment, the processor 101 may be a Central Processing Unit (CPU), a microprocessor, a Digital Signal Processor (DSP), an application-specific integrated circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof, which is capable of implementing or executing various exemplary logic blocks, modules, and circuits described in connection with the disclosure of the embodiment of the present invention; a processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, a DSP and a microprocessor, or the like.
Optionally, the processor 101 of the server 10 includes one or more CPUs, which are single-core CPUs (single-CPUs) or multi-core CPUs (multi-CPUs).
The memory 102 includes, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical memory, or the like. The memory 102 holds the code of the operating system.
Optionally, the processor 101 reads the instructions stored in the memory 102 to implement the network optimization method in the embodiment of the present invention, or the processor 101 implements the network optimization method provided in the embodiment of the present invention by using the instructions stored inside. In the case that the processor 101 implements the network optimization method provided by the embodiment of the present invention by reading the execution saved in the memory, the memory stores instructions for implementing the network optimization method provided by the embodiment of the present invention.
The network interface 103 is a wired interface, such as a Fiber Distributed Data Interface (FDDI) interface or a Gigabit Ethernet (GE) interface. Alternatively, the network interface 103 is a wireless interface. The network interface 103 is used for the server 10 to communicate with other devices.
The memory 102 is used for storing the sample data of a plurality of sample time slots corresponding to the target cell. The at least one processor 101 further executes the method described in the embodiments of the present invention according to the sample data of the plurality of sample time slots corresponding to the target cell stored in the memory 102. For more details of the above functions implemented by the processor 101, reference is made to the following description of various method embodiments.
Optionally, the server 10 further includes a bus, and the processor 101 and the memory 102 are connected to each other through the bus 104, or in other manners.
Optionally, the server 10 further comprises an input/output interface 105, wherein the input/output interface 105 is configured to connect with an input device, and receive a network optimization request input by a user through the input device. Input devices include, but are not limited to, a keyboard, a touch screen, a microphone, and the like. The input/output interface 105 is further configured to connect with an output device, and output the network optimization result of the processor 101 (i.e., determine to perform network optimization on the target cell). Output devices include, but are not limited to, a display, a printer, and the like.
As shown in fig. 2, the network optimization method provided by the embodiment of the present invention may include S101 to S103.
S101, acquiring sampling data of a plurality of sampling time slots corresponding to a target cell.
The sampling data of one sampling time slot comprises the number of PRBs occupied by at least one user group, the number of transmission layers occupied by the at least one user group and the number of PRBs occupied by the at least one user group on a j-layer transmission layer, wherein j is an integer greater than or equal to 2, and one user group comprises at least two UEs.
It should be understood that a network device (e.g., a base station) may collect the sample data of the plurality of sample time slots and send the sample data to a server executing the network optimization method provided by the present invention, i.e., the server may obtain the sample data of the plurality of sample time slots from the network device.
Specifically, the UE in each user group performs data transmission in the MU-MIMO mode, where the number of PRBs occupied by one user group indicates the size of frequency domain resources occupied by the (UE in) user group, the number of transmission layers occupied by one user group indicates the size of space domain resources occupied by the (UE in) user group, and the number of PRBs occupied by one user group on j transmission layers can also be understood as how many PRBs occupy j transmission layers in the user group.
For example, as shown in fig. 3, a data transmission diagram corresponding to sample data of a certain sample slot in multiple sample slots is shown, where the communication system corresponding to the sample slot includes user group 1 and user group 2, the user group 1 includes UE1, UE2, and UE3, the user group 2 includes UE4, UE5, and UE6, an abscissa represents a size of frequency domain resources occupied by each user group (including user group 1 and user group 2), a unit is PRB, and an ordinate represents a size of space domain resources occupied by each user group, and a unit is a transmission layer.
As can be determined from fig. 3, the number of PRBs occupied by user group 1 is 7, that is, user group 1 occupies 7 PRBs, and the number of transmission layers occupied by user group 1 is 5 (where UE1 occupies 3 transmission layers including UE1 RANK1, UE1 RANK2, and UE1 RANK3, UE2 occupies 1 transmission layer including UE2 RANK1, and UE3 occupies 1 transmission layer including UE3 RANK1), that is, user group 1 occupies 5 transmission layers, and the number of PRBs occupied by user group 1 on 5 transmission layers is 7, that is, 7 PRBs in user group 1 all occupy 5 transmission layers. Similarly, the number of PRBs occupied by the user group 2 is 4, the number of transmission layers occupied by the user group 2 is 4 (wherein, the UE4 occupies 2 transmission layers including the UE4 RANK1 and the UE4 RANK2, the UE5 occupies 1 transmission layer including the UE5 RANK1, the UE6 occupies 1 transmission layer including the UE6 RANK1), the number of PRBs occupied by the user group 2 on the 4 transmission layers is 3, and the number of PRBs occupied by the user group 2 on the 3 transmission layers is 1.
It should be noted that the number of PRBs occupied by a user group is the maximum value of the PRBs occupied by the UE in the user group in the layer 1 transmission layer. The fact that 1 PRB occupies j transmission layers means that the maximum value of the transmission layers occupied by the PRB is j.
For example, as shown in fig. 3, for user group 2, if the number of PRBs occupied by UE4 and UE5 in the layer 1 transport layer is 4 and the number of PRBs occupied by UE6 in the layer one transport layer is 3, the network optimization device may determine that the number of PRBs occupied by user group 2 is 4. And 3 PRBs occupy 4 transmission layers, that is, in the user group 2, the maximum value of the transmission layers occupied by the first 3 PRBs from left to right is 4, and similarly, the maximum value of the transmission layers occupied by the remaining 1 PRB in the user group 2 (that is, the rightmost one PRB in the user group 2) is 3, that is, the number of the PRBs occupied by the user group 2 on the 3 transmission layers is 1.
S102, determining the number of MU-MIMO pairing layers corresponding to the target cell according to the sampling data of each sampling time slot in the plurality of sampling time slots.
The MU-MIMO pairing layer number corresponding to the target cell is used for representing the number of the UE which can perform data transmission on the same time-frequency resource by the target cell.
In an implementation manner of the embodiment of the present invention, the number of MU-MIMO pairing layers corresponding to the target cell is an average value of the number of MU-MIMO pairing layers of each sampling time slot in the plurality of sampling time slots.
The number of MU-MIMO paired layers of the t-th sampling time slot corresponding to the target cell meets the following requirements:
Figure BDA0002737025380000071
wherein, XtThe number of MU-MIMO paired layers of the t-th sampling time slot corresponding to the target cell is represented, t is an integer larger than or equal to 1, N represents the number of user groups corresponding to the t-th sampling time slot, N is an integer larger than or equal to 1, and LiIndicates the number of transmission layers occupied by the ith user group corresponding to the t-th sampling time slot, PRBiIndicating the number of PRBs occupied by the ith user group, PRBi,jAnd indicating the number of PRBs of the j-layer transmission layer occupied by the ith user group, wherein i is an integer greater than or equal to 1, j is an integer greater than or equal to 2, and the tth sampling slot is one of the plurality of sampling slots.
Further, it can be determined
Figure BDA0002737025380000072
It should be understood that the number of PRBs occupied by the ith user group is the sum of the number of PRBs occupied by the ith user group on the j-layer transmission layer. For example, for user group 2 shown in FIG. 3, it occupiesThe number of PRBs used is 4, and the number of PRBs of the user group 2 occupying the 4-layer transmission layer and the number of PRBs of the user group 2 occupying the 3-layer transmission layer are 3 and 1, respectively.
For example, combining the above formula and the example in S101, it may be determined that the number of MU-MIMO pairing layers of the sampling slot corresponding to the target cell is 4.55 (i.e., the number of MU-MIMO pairing layers is 4.55)
Figure BDA0002737025380000073
) And (3) a layer.
Thus, after the network optimization device determines the number of MU-MIMO pairing layers of each sampling time slot in the plurality of sampling time slots, the average value of the number of MU-MIMO pairing layers of each sampling time slot in the plurality of sampling time slots is determined as the number of MU-MIMO pairing layers corresponding to the target cell.
S103, determining to carry out network optimization on the target cell under the condition that the MU-MIMO pairing layer number corresponding to the target cell is less than or equal to the MU-MIMO pairing layer number threshold.
It should be understood that a single UE may employ two antennas for data transmission, and a single base station may employ 8 antennas for data transmission, and thus, data transmission may be performed between the single UE and the single base station in a 2-to-2 manner (i.e., 2 antennas to 2 antennas), so that it may be determined that the number of MU-MIMO pairing layers corresponding to the target cell should be at least 2, i.e., in an implementation, the above-mentioned MU-MIMO pairing layer number threshold is 2. When the number of MU-MIMO paired layers corresponding to the target cell is less than or equal to 2, it is indicated that the UE in the target cell does not use the MU-MIMO mode for data transmission, or it is determined that the transmission effect is not ideal enough when the MU-MIMO mode is used for data transmission, that is, the transmission effect is similar to the transmission effect when the UE uses a single-user multiple-input multiple-output (SU-MIMO) mode for data transmission. In this way, the network optimization device determines that network optimization needs to be performed on the target cell, so that the number of MU-MIMO pairing layers corresponding to the target cell can be greater than the threshold value of the number of MU-MIMO pairing layers.
Correspondingly, under the condition that the number of MU-MIMO paired layers corresponding to the target cell is greater than the threshold of the number of MU-MIMO paired layers, the network optimization device can determine that the target cell has sufficient application to frequency domain resources and space domain resources, namely, the UE in the target cell has a transmission effect when data transmission is carried out in an MU-MIMO mode.
In the network optimization method provided in the embodiment of the present invention, a network optimization device obtains sampling data of a plurality of sampling slots corresponding to a target cell, where the sampling data of one sampling slot includes the number of PRBs occupied by at least one user group, the number of transmission layers occupied by the at least one user group, and the number of transmission layers occupied by the at least one user group; then, the network optimization device determines the number of MU-MIMO paired layers corresponding to the target cell according to the sampling data of each sampling time slot in the plurality of sampling time slots, wherein the number of MU-MIMO paired layers corresponding to the target cell is used for representing the number of UE (user equipment) which can perform data transmission on the same time-frequency resource of the target cell; and determining to perform network optimization on the target cell under the condition that the MU-MIMO pairing layer number corresponding to the target cell is less than or equal to the MU-MIMO pairing layer number threshold. In the embodiment of the invention, the network optimization device can determine the number of MU-MIMO paired layers corresponding to the target cell according to the occupation situation of the two resources based on the frequency domain resource occupation situation of the target cell and the space domain resource occupation situation of the target cell, and then determine whether the network optimization needs to be carried out on the target cell, and can determine whether the application of the MU-MIMO transmission mode in the target cell is effective, thereby improving the efficiency of data transmission in the network.
With reference to fig. 2, as shown in fig. 4, the network optimization method provided in the embodiment of the present invention further includes: S104-S107.
And S104, determining the maximum value of the transmission layer number corresponding to each time slot in the plurality of sampling time slots.
The maximum value of the number of transmission layers corresponding to the target sampling time slot is the maximum value of the number of transmission layers occupied by at least one user group included in the sampling data of the target sampling time slot and the number of transmission layers occupied by at least one UE included in the sampling data of the target sampling time slot, and the target sampling time slot is one of the plurality of sampling time slots.
It should be understood that the sample data of one sample time slot corresponding to the target cell further includes the number of transmission layers occupied by at least one UE, where the at least one UE is a UE that performs data transmission in SU-MIMO mode.
In the embodiment of the present invention, the network optimization device determines the maximum value of the number of transmission layers occupied by at least one user group and at least one UE corresponding to the target sampling time slot as the maximum value of the number of transmission layers corresponding to the target sampling time slot.
Illustratively, as shown in fig. 3, the UE7 is a UE that performs data transmission in SU-MIMO mode, and the number of transmission layers occupied by the UE7 is 2, and the number of PRBs occupied by the UE is 4.
With reference to the example in S101, assuming that the sample slot corresponding to the data transmission diagram shown in fig. 3 is the target sample slot, the network optimization device may determine that the maximum value of the number of transmission layers corresponding to the target sample slot is 5.
And S105, determining the available transmission layer number corresponding to the target cell according to the maximum value of the transmission layer number corresponding to each sampling time slot in the plurality of sampling time slots.
The available transmission layer number corresponding to the target cell is used for representing the maximum transmission layer number when the target cell performs data transmission.
In an implementation manner of the embodiment of the present invention, the network optimization device may determine a maximum value (i.e., a maximum number of transmission layers) of maximum values of the number of transmission layers corresponding to each of the plurality of sampling time slots as the available number of transmission layers corresponding to the target cell.
In another implementation manner of the embodiment of the present invention, in consideration of different geographical environments of different base stations and influences of electromagnetic wave reflection, etc., a dynamically changing available transmission layer number may be configured for each sampling time slot, and then the available transmission layer number corresponding to the target cell is determined according to the available transmission layer number corresponding to each sampling time slot. Specifically, the method can be realized by the following steps A to B.
And step A, determining the available transmission layer number corresponding to each sampling time slot in the plurality of sampling time slots according to the maximum value of the transmission layer number corresponding to each sampling time slot in the plurality of sampling time slots.
The number of available transmission layers corresponding to the tth sampling time slot satisfies the following conditions:
Figure BDA0002737025380000091
specifically, YtIndicates the available transmission layer number, L, corresponding to the t-th sampling time slott_maxRepresents the maximum value of the number of transmission layers, L, corresponding to the t-th sampling slott-1_maxRepresents the maximum value, L, of the number of transmission layers corresponding to the t-1 th sampling slotu_maxThe maximum value of the transmission layer number corresponding to the u-th sampling time slot is shown, w represents the size of a preset time window, and the t-th sampling time slot is one of the plurality of sampling time slots.
It should be understood that the network optimization device may configure a preset time window size (i.e., w) for the plurality of sampling slots, and when t is greater than or equal to 1 and less than w, it indicates that the t-th sampling slot is located before the first preset time window (the first preset time window may be understood as the first preset time window, i.e., the preset time window formed when t is equal to w), the network optimization device may determine the largest value among the maximum values of the number of transmission layers corresponding to the t-th sampling slot and the maximum value of the number of transmission layers corresponding to the t-1-th sampling slot (i.e., the sampling slot adjacent to and before the t-th sampling slot) as the available number of transmission layers corresponding to the t-th sampling slot.
When t is greater than or equal to w, it indicates that the t-th sampling time slot is located within a certain preset time window (which may be the first preset time window, or other preset time windows), the network optimization device may determine, as the available transmission layer number corresponding to the t-th sampling time slot, the largest value among the maximum values of the transmission layer numbers respectively corresponding to w +1 sampling time slots between the t-w-th sampling time slot (i.e., the first sampling time slot within the preset time window) and the t-th sampling time slot (i.e., the last sampling time slot within the preset time window).
It should be understood that the size of the preset time window may be adjusted according to the communication state of the target cell, for example, when the flow of the people in the target cell is large and the communication service changes faster, a smaller preset time window may be configured for the target cell, otherwise, a larger preset time window may be configured for the target cell.
It should be noted that, although the size of the preset time window is a fixed value, in an actual application process, the preset time window changes with the change of a certain sampling time slot. For example, when t is equal to w, the preset time window corresponding to the t-th sampling time slot is the first preset time window, and when t is equal to w +1, the preset time window corresponding to the t-th sampling time slot is the second preset time window, that is, the preset time window adjacent to the first preset time window between the first preset time windows.
Illustratively, as shown in fig. 5, the maximum value of the number of transmission layers corresponding to each of the 12 sampling slots corresponding to the target cell is shown. In fig. 5, the abscissa indicates the t-th sampling slot, t is 1, 2, 3, …, and 12, and the ordinate indicates the maximum value of the number of transmission layers corresponding to the t-th sampling slot, specifically, the maximum values of the number of transmission layers corresponding to the 1 st to 12 th sampling slots are 5, 7, 6, 4, 3, 1, 2, 3, 2, and 1, respectively.
In conjunction with the above formula in step a, assuming that w is 4, when t is 0, the number of available transmission layers (i.e. the number of available transmission layers corresponding to the 0 th sampling slot) is set to 2 by default, and the network optimization device may determine the number of available transmission layers corresponding to each of the 12 sampling slots as shown in fig. 6.
Specifically, the number of available transmission layers corresponding to the 1 st sampling slot (i.e., when t is 1) is 5, the number of available transmission layers corresponding to the 2 nd sampling slot (i.e., when t is 2) is 7, the number of available transmission layers corresponding to the 3 rd sampling slot (i.e., when t is 3) is 7 (i.e., the maximum value of the number of transmission layers corresponding to the 3 rd sampling slot in fig. 5 is the maximum value of the maximum values of the number of transmission layers corresponding to the 2 nd sampling slot), the 4 th sampling slot (i.e., when t is 4, when t is w) is 7 (the time window corresponding to the 4 th sampling slot is the first preset time window, i.e., sampling slots 0 to 4, the maximum value of the maximum values of the corresponding transmission slots is selected from the first preset time window as the number of available transmission layers corresponding to the 4 th sampling slot), and the number of available transmission layers corresponding to the 5 th sampling slot (i.e., when t is 5), is 7 (the number of available transmission layers corresponding to the 5 th sampling slot is the second time window) Presetting a time window, namely sampling time slots 1-5, selecting the maximum value of the maximum values of the corresponding transmission layer numbers from the second preset time window as the available transmission layer number corresponding to the 5 th sampling time slot, and in the same way, the available transmission layer numbers corresponding to the 6 th sampling time slot to the 12 th sampling time slot are respectively: 7. 6, 4, 3 and 3.
And step B, determining the average value of the available transmission layer numbers corresponding to the sampling time slots in the plurality of sampling time slots as the available transmission layer number corresponding to the target cell.
Illustratively, in conjunction with the example in fig. 6 above, the number of available transmission layers for the target cell is 5.42 (i.e., the target cell has a corresponding number of available transmission layers)
Figure BDA0002737025380000111
) And (3) a layer.
And S106, determining the MU-MIMO pairing effectiveness corresponding to the target cell.
The MU-MIMO pairing effectiveness corresponding to the target cell is the ratio of the MU-MIMO pairing layer number corresponding to the target cell to the available transmission layer number corresponding to the target cell, and the MU-MIMO pairing effectiveness corresponding to the target cell is used for representing the network load condition of the target cell.
Specifically, the MU-MIMO pairing validity corresponding to the target cell satisfies:
Figure BDA0002737025380000112
wherein, Z represents the MU-MIMO pairing effectiveness corresponding to the target cell, X represents the MU-MIMO pairing layer number corresponding to the target cell, and Y represents the available transmission layer number corresponding to the target cell.
S107, determining to carry out network optimization on the target cell under the condition that the MU-MIMO pairing effectiveness corresponding to the target cell is greater than the MU-MIMO pairing effectiveness threshold.
It should be understood that when the MU-MIMO pairing validity corresponding to the target cell is greater than the MU-MIMO pairing validity threshold, it indicates that the network load of the target cell is large, and network optimization needs to be performed on the target cell. Specifically, it can be understood that the spatial domain resource of the target cell is insufficient, and the network load of the target cell can be reduced by increasing the spatial domain resource, for example, extending a base station or guiding the UE in the target cell to access a base station of another cell.
In the network optimization method provided by the embodiment of the present invention, the network optimization device determines the number of MU-MIMO paired layers corresponding to the target cell based on the frequency domain resources and the space domain resources, determines the number of available transmission layers corresponding to the target cell according to the maximum value of the number of transmission layers corresponding to each sampling time slot in the plurality of sampling time slots (i.e., different, dynamically changing, number of available transmission layers corresponding to different sampling time slots), and then determines the MU-MIMO pairing effectiveness corresponding to the target cell according to the number of MU-MIMO paired layers corresponding to the target cell and the number of available transmission layers corresponding to the target cell. The network load of the target cell can be accurately and reasonably determined, and the network transmission efficiency is improved.
In the embodiment of the present invention, the network optimization device and the like may be divided into functional modules according to the above method examples, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, the division of the modules in the embodiment of the present invention is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
In the case of dividing each functional module by corresponding functions, fig. 7 shows a schematic structural diagram of a possible network optimization device according to the foregoing embodiment, and as shown in fig. 7, the network optimization device 20 may include: an acquisition module 201 and a determination module 202.
An obtaining module 201, configured to obtain sample data of multiple sample time slots corresponding to a target cell, where the sample data of one sample time slot includes a number of PRBs occupied by at least one user group, a number of transmission layers occupied by the at least one user group, and a number of PRBs occupied by the at least one user group on a j-layer transmission layer, j is an integer greater than or equal to 2, and one user group includes at least two user equipment UEs.
A determining module 202, configured to determine, according to the sampling data of each sampling time slot in the multiple sampling time slots, the number of MU-MIMO pairing layers corresponding to the target cell, where the number of MU-MIMO pairing layers corresponding to the target cell is used to represent the number of UEs that can perform data transmission on the same time-frequency resource in the target cell.
The determining module 202 is further configured to determine to perform network optimization on the target cell when the number of MU-MIMO pairing layers corresponding to the target cell is less than or equal to the MU-MIMO pairing number-of-layers threshold.
Optionally, the sampled data of one sample slot further includes the number of transmission layers occupied by at least one UE.
The determining module 202 is further configured to determine a maximum value of the number of transmission layers corresponding to each of the plurality of sampling time slots, where the maximum value of the number of transmission layers corresponding to a target sampling time slot is a maximum value of the number of transmission layers occupied by at least one user group included in the sampling data of the target sampling time slot and the number of transmission layers occupied by at least one UE included in the sampling data of the target sampling time slot, and the target sampling time slot is one of the plurality of sampling time slots.
The determining module 202 is further configured to determine, according to a maximum value of the number of transmission layers corresponding to each of the plurality of sampling time slots, an available number of transmission layers corresponding to the target cell, where the available number of transmission layers corresponding to the target cell is used to represent a maximum number of transmission layers when the target cell performs data transmission.
The determining module 202 is further configured to determine MU-MIMO pairing validity corresponding to the target cell, where the MU-MIMO pairing validity corresponding to the target cell is a ratio of the number of MU-MIMO pairing layers corresponding to the target cell to the number of available transmission layers corresponding to the target cell, and the MU-MIMO pairing validity corresponding to the target cell is used to characterize a network load condition of the target cell.
The determining module 202 is further configured to determine to perform network optimization on the target cell when the MU-MIMO pairing validity corresponding to the target cell is greater than the MU-MIMO pairing validity threshold.
Optionally, the number of MU-MIMO pairing layers corresponding to the target cell is an average value of the number of MU-MIMO pairing layers of each sampling time slot in the plurality of sampling time slots.
The number of MU-MIMO paired layers of the t-th sampling time slot corresponding to the target cell meets the following requirements:
Figure BDA0002737025380000131
wherein, XtIndicating the number of MU-MIMO paired layers of the t-th sampling time slot corresponding to the target cell, t being an integer greater than or equal to 1, N indicating the number of user groups corresponding to the t-th sampling time slot, N being an integer greater than or equal to 1, LiIndicates the number of transmission layers occupied by the ith user group corresponding to the t-th sampling time slot, PRBiIndicates the number of PRBs occupied by the i-th user group, PRBi,jAnd indicating the number of PRBs of the j-layer transmission layer occupied by the ith user group, wherein i is an integer greater than or equal to 1, j is an integer greater than or equal to 2, and the tth sampling slot is one of the plurality of sampling slots.
Optionally, the determining module 202 is specifically configured to determine, according to a maximum value of the number of transmission layers corresponding to each sample time slot in the plurality of sample time slots, an available number of transmission layers corresponding to each sample time slot in the plurality of sample time slots.
The number of available transmission layers corresponding to the t-th sampling time slot satisfies:
Figure BDA0002737025380000132
wherein, YtIndicates the available transmission layer number, L, corresponding to the t-th sampling time slott_maxRepresents the maximum value, L, of the transmission layer number corresponding to the t-th sampling time slott-1_maxRepresents the maximum value, L, of the number of transmission layers corresponding to the t-1 th sampling slotu_maxThe maximum value of the transmission layer number corresponding to the u-th sampling time slot is represented, w represents the size of a preset time window, and the t-th sampling time slot is one of the multiple sampling time slots.
The determining module 202 is further specifically configured to determine an average value of the available number of transmission layers corresponding to each of the plurality of sampling time slots as the available number of transmission layers corresponding to the target cell.
In the case of an integrated unit, fig. 8 shows a schematic diagram of a possible structure of the network optimization device according to the above embodiment. As shown in fig. 8, the network optimization device 30 may include: a processing module 301 and a communication module 302. The processing module 301 may be used to control and manage the actions of the network optimization device 30. The communication module 302 may be used to support communication of the network optimization device 30 with other entities. Optionally, as shown in fig. 8, the network optimization device 30 may further include a storage module 303 for storing program codes and data of the network optimization device 30.
The processing module 301 may be a processor or a controller (e.g., the processor 101 shown in fig. 1). The communication module 302 may be a transceiver, a transceiver circuit, or a communication interface, etc. (e.g., may be the network interface 103 shown in fig. 1 described above). The storage module 303 may be a memory (e.g., may be the memory 102 described above with reference to fig. 1).
When the processing module 301 is a processor, the communication module 302 is a transceiver, and the storage module 303 is a memory, the processor, the transceiver, and the memory may be connected via a bus. The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions described in accordance with the embodiments of the invention are all or partially effected when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optics, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or can comprise one or more data storage devices, such as a server, a data center, etc., that can be integrated with the medium. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A method for network optimization, comprising:
acquiring sampling data of a plurality of sampling time slots corresponding to a target cell, wherein the sampling data of one sampling time slot comprises the number of PRBs (physical resource blocks) occupied by at least one user group, the number of transmission layers occupied by the at least one user group and the number of PRBs of a j-layer transmission layer occupied by the at least one user group, j is an integer greater than or equal to 2, and one user group comprises at least two User Equipment (UE);
determining the number of multi-user multi-input multi-output (MU-MIMO) paired layers corresponding to the target cell according to the sampling data of each sampling time slot in the plurality of sampling time slots, wherein the MU-MIMO paired layers corresponding to the target cell are used for representing the number of User Equipment (UE) capable of performing data transmission on the same time-frequency resource of the target cell;
and determining to perform network optimization on the target cell under the condition that the number of MU-MIMO paired layers corresponding to the target cell is less than or equal to the threshold of the number of MU-MIMO paired layers.
2. The method of claim 1, wherein the sample data of the one sample slot further comprises a number of transmission layers occupied by at least one UE;
after determining the number of MU-MIMO pairing layers corresponding to the target cell according to the sampling data of each of the plurality of sampling slots, the method further includes:
determining a maximum value of the number of transmission layers corresponding to each sampling time slot in the plurality of sampling time slots, wherein the maximum value of the number of transmission layers corresponding to a target sampling time slot is a maximum value of the number of transmission layers occupied by at least one user group included in sampling data of the target sampling time slot and the number of transmission layers occupied by at least one UE included in sampling data of the target sampling time slot, and the target sampling time slot is one of the plurality of sampling time slots;
determining the number of available transmission layers corresponding to the target cell according to the maximum value of the number of transmission layers corresponding to each sampling time slot in the plurality of sampling time slots, wherein the number of available transmission layers corresponding to the target cell is used for representing the maximum number of transmission layers when the target cell performs data transmission;
determining MU-MIMO pairing validity corresponding to the target cell, wherein the MU-MIMO pairing validity corresponding to the target cell is a ratio of the MU-MIMO pairing layer number corresponding to the target cell to an available transmission layer number corresponding to the target cell, and the MU-MIMO pairing validity corresponding to the target cell is used for representing a network load condition of the target cell;
and determining to perform network optimization on the target cell under the condition that the MU-MIMO pairing validity corresponding to the target cell is greater than the MU-MIMO pairing validity threshold.
3. The method of claim 2, wherein the number of MU-MIMO pairing layers corresponding to the target cell is an average value of the number of MU-MIMO pairing layers of each of the plurality of sampling slots;
the number of MU-MIMO paired layers of the t-th sampling time slot corresponding to the target cell meets the following requirements:
Figure FDA0002737025370000021
wherein, XtRepresenting the number of MU-MIMO paired layers of the t-th sampling time slot corresponding to the target cell, wherein t is an integer greater than or equal to 1, N represents the number of user groups corresponding to the t-th sampling time slot, N is an integer greater than or equal to 1, and LiIndicating the number of transmission layers occupied by the ith user group corresponding to the t-th sampling time slot, PRBiIndicating the number of PRBs occupied by the ith user group, PRBsi,jAnd indicating the number of PRBs of a j-layer transmission layer occupied by the ith user group, wherein i is an integer greater than or equal to 1, j is an integer greater than or equal to 2, and the tth sampling slot is one of the multiple sampling slots.
4. The method of claim 3, wherein the determining the number of available transmission layers corresponding to the target cell according to the maximum number of transmission layers corresponding to each of the plurality of sampling slots comprises:
determining an available transmission layer number corresponding to each sampling time slot in the plurality of sampling time slots according to the maximum value of the transmission layer number corresponding to each sampling time slot in the plurality of sampling time slots;
the number of available transmission layers corresponding to the tth sampling time slot satisfies:
Figure FDA0002737025370000022
wherein, YtIndicating the available transmission layer number L corresponding to the t-th sampling time slott_maxRepresents the maximum value L of the transmission layer number corresponding to the t-th sampling time slott-1_maxRepresents the maximum value, L, of the number of transmission layers corresponding to the t-1 th sampling slotu_maxThe maximum value of the transmission layer number corresponding to the u-th sampling time slot is represented, w represents the size of a preset time window, and the t-th sampling time slot isThe sampling time slot is one of the plurality of sampling time slots;
and determining the average value of the available transmission layer numbers corresponding to the sampling time slots in the plurality of sampling time slots as the available transmission layer number corresponding to the target cell.
5. The network optimization device is characterized by comprising an acquisition module and a determination module;
the acquiring module is configured to acquire sampling data of multiple sampling slots corresponding to a target cell, where the sampling data of one sampling slot includes the number of physical resource blocks PRB occupied by at least one user group, the number of transmission layers occupied by the at least one user group, and the number of PRBs occupied by the at least one user group on a j-layer transmission layer, j is an integer greater than or equal to 2, and one user group includes at least two user equipment UE;
the determining module is configured to determine, according to the sampling data of each sampling time slot in the multiple sampling time slots, the number of MU-MIMO paired layers corresponding to the target cell, where the number of MU-MIMO paired layers corresponding to the target cell is used to represent the number of UEs capable of performing data transmission on the same time-frequency resource in the target cell;
the determining module is further configured to determine to perform network optimization on the target cell when the number of MU-MIMO paired layers corresponding to the target cell is less than or equal to a MU-MIMO paired layer number threshold.
6. The network optimization device of claim 5, wherein the sampled data of the one sampled slot further comprises a number of transmission layers occupied by at least one UE;
the determining module is further configured to determine a maximum value of the number of transmission layers corresponding to each of the plurality of sampling time slots, where the maximum value of the number of transmission layers corresponding to a target sampling time slot is a maximum value of the number of transmission layers occupied by at least one user group included in sampling data of the target sampling time slot and the number of transmission layers occupied by at least one UE included in sampling data of the target sampling time slot, and the target sampling time slot is one of the plurality of sampling time slots;
the determining module is further configured to determine, according to a maximum value of the number of transmission layers corresponding to each of the plurality of sampling time slots, the number of available transmission layers corresponding to the target cell, where the number of available transmission layers corresponding to the target cell is used to represent a maximum number of transmission layers when the target cell performs data transmission;
the determining module is further configured to determine MU-MIMO pairing validity corresponding to the target cell, where the MU-MIMO pairing validity corresponding to the target cell is a ratio of the number of MU-MIMO pairing layers corresponding to the target cell to the number of available transmission layers corresponding to the target cell, and the MU-MIMO pairing validity corresponding to the target cell is used to characterize a network load condition of the target cell;
the determining module is further configured to determine to perform network optimization on the target cell when the MU-MIMO pairing validity corresponding to the target cell is greater than a MU-MIMO pairing validity threshold.
7. The network optimization device according to claim 6, wherein the number of MU-MIMO pairing layers corresponding to the target cell is an average value of the number of MU-MIMO pairing layers of each of the plurality of sampling slots;
the number of MU-MIMO paired layers of the t-th sampling time slot corresponding to the target cell meets the following requirements:
Figure FDA0002737025370000041
wherein, XtRepresenting the number of MU-MIMO paired layers of the t-th sampling time slot corresponding to the target cell, wherein t is an integer greater than or equal to 1, N represents the number of user groups corresponding to the t-th sampling time slot, N is an integer greater than or equal to 1, and LiIndicating the number of transmission layers occupied by the ith user group corresponding to the t-th sampling time slot, PRBiIndicating the ith user accountNumber of PRBs used, PRBsi,jAnd indicating the number of PRBs of a j-layer transmission layer occupied by the ith user group, wherein i is an integer greater than or equal to 1, j is an integer greater than or equal to 2, and the tth sampling slot is one of the multiple sampling slots.
8. The network optimization device of claim 7,
the determining module is specifically configured to determine, according to a maximum value of the number of transmission layers corresponding to each of the plurality of sampling time slots, the number of available transmission layers corresponding to each of the plurality of sampling time slots;
the number of available transmission layers corresponding to the tth sampling time slot satisfies:
Figure FDA0002737025370000042
wherein, YtIndicating the available transmission layer number L corresponding to the t-th sampling time slott_maxRepresents the maximum value L of the transmission layer number corresponding to the t-th sampling time slott-1_maxRepresents the maximum value, L, of the number of transmission layers corresponding to the t-1 th sampling slotu_maxThe maximum value of the transmission layer number corresponding to the u-th sampling time slot is represented, w represents the size of a preset time window, and the t-th sampling time slot is one of the plurality of sampling time slots;
the determining module is specifically further configured to determine an average value of the number of available transmission layers corresponding to each of the plurality of sampling time slots as the number of available transmission layers corresponding to the target cell.
9. A network optimization device, the network optimization device comprising: a processor, a memory, a bus, and a communication interface; the memory is used for storing computer-executable instructions, and when the network optimization device runs, the processor executes the computer-executable instructions stored in the memory, so that the network optimization device executes the network optimization method according to any one of claims 1 to 4.
10. A computer-readable storage medium comprising a computer program which, when run on a computer, causes the computer to perform the network optimization method of any one of claims 1 to 4.
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