WO2020063921A1 - 无线网络自主优化方法、装置、设备和介质 - Google Patents

无线网络自主优化方法、装置、设备和介质 Download PDF

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Publication number
WO2020063921A1
WO2020063921A1 PCT/CN2019/108742 CN2019108742W WO2020063921A1 WO 2020063921 A1 WO2020063921 A1 WO 2020063921A1 CN 2019108742 W CN2019108742 W CN 2019108742W WO 2020063921 A1 WO2020063921 A1 WO 2020063921A1
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cell
network
target
signal strength
sample
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PCT/CN2019/108742
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English (en)
French (fr)
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王佳煜
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闻泰通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data

Definitions

  • the embodiments of the present application relate to the field of computer technology, for example, a method, an apparatus, a device, and a medium for autonomously optimizing a wireless network.
  • a wireless coverage area there are usually many cells providing services.
  • a wireless coverage area usually has many cells in the same frequency band, different frequency bands, and different wireless access technologies with overlapping coverage.
  • the terminal when the terminal resides in these areas where multiple signals overlap and acquires network services, the terminal has always occupied a poor cell to obtain services due to improper network mobility parameter settings or network mismatches.
  • users experience the Internet service in this situation, they will notice that the webpage cannot be opened, the Internet is slow, and the game is stalled.
  • wireless network optimization is mainly performed manually by wireless network engineers. After receiving user optimization requests, wireless network engineers perform traffic data analysis, field test data collection, parameter analysis, and hardware inspection on the running network. , Find out the reasons that affect the quality of the network, and ensure that the system can operate normally through parameter modification, network structure adjustment, equipment configuration adjustment, and other technical means.
  • the embodiments of the present application provide a wireless network autonomous optimization method, device, device, and medium, so as to avoid optimization of the wireless network by manual methods in the related art, with accompanying low detection efficiency, long maintenance time, and poor user experience on the Internet.
  • an embodiment of the present application provides a method for autonomously optimizing a wireless network.
  • the method includes: detecting a target network of a candidate cell when a network signal strength of a current serving cell is lower than a network signal strength threshold.
  • Characteristic value wherein the target network characteristic value includes network signal strength, cell bandwidth value, sampling point value and cell frequency point magnification value; according to the target network characteristic value of the candidate cell and the target network characteristic value target Coefficient to determine the target network quality of the candidate cell; select the target candidate cell from the candidate cells according to the target network quality of the candidate cell; according to the network signal strength of the current serving cell and the target candidate For the network signal strength of the cell, choose the residential cell.
  • an embodiment of the present application provides a wireless network autonomous optimization device, which includes a target network characteristic value detection module, a target network quality determination module, a target candidate cell selection module, and a cell resident module.
  • a target network characteristic value detection module is configured to detect a target network characteristic value of a candidate cell when a network signal strength of a current serving cell is lower than a network signal strength threshold, wherein the target network characteristic value includes a network signal strength , The cell bandwidth value, the sampling point value, and the cell frequency point magnification value; the target network quality determination module is configured to determine the candidate cell based on the target network characteristic value of the candidate cell and the target coefficient of the target network characteristic value.
  • Target network quality a target candidate cell selection module configured to select a target candidate cell from the candidate cells according to the target network quality of the candidate cell; a cell resident module configured to be configured according to the current service The network signal strength of the cell and the network signal strength of the target candidate cell select a camping cell.
  • an embodiment of the present application provides a device including: at least one processor; a storage device configured to store at least one program, and when the at least one program is executed by the at least one processor, the at least one The processor implements a wireless network autonomous optimization method as described in any embodiment of the present application.
  • an embodiment of the present application provides a computer-readable medium on which a computer program is stored.
  • the computer program is executed by a processor, the wireless network autonomous optimization method according to any one of the embodiments of the present application is implemented. .
  • FIG. 1 is a flowchart of a wireless network autonomous optimization method according to an embodiment of the present application
  • FIG. 2 is a flowchart of another wireless network autonomous optimization method according to an embodiment of the present application.
  • FIG. 3 is a schematic structural diagram of a wireless network autonomous optimization device according to an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of a device according to an embodiment of the present application.
  • FIG. 1 is a flowchart of a method for autonomously optimizing a wireless network according to an embodiment of the present application. This embodiment is applicable to a situation where a network signal strength of a current serving cell where a user is located is poor.
  • the network autonomously optimizes the device for execution.
  • the device can be integrated into a wireless router, a mobile terminal (such as a cell phone, a smartphone, a tablet computer) and a fixed terminal (such as a desktop computer or a notebook computer).
  • the method includes Steps 101 to 104.
  • step 101 when the network signal strength of the current serving cell is lower than the network signal strength threshold, a target network characteristic value of the candidate cell is detected.
  • the current serving cell refers to the serving cell in which the terminal or router used by the user is currently located.
  • the strength of the network line number includes but is not limited to at least one of the following parameters: Reference Signal Received Power (RSRP) parameters, signals And interference plus noise ratio (Signal, Interference, Noise, Ratio, SINR) parameters, reference signal reception quality (Reference, Signaling, Quality, RSRQ) parameters, etc.
  • RSRP Reference Signal Received Power
  • SINR signals And interference plus noise ratio
  • Reference signal reception quality Reference, Signaling, Quality, RSRQ
  • the network signal strength of the current serving cell is higher than or equal to the network signal strength threshold, then It is considered that the current serving cell network is in good condition and continues to reside in the current serving cell; if the current serving cell network signal strength is lower than the network signal strength threshold, it is considered that the current serving cell network condition is poor and it is necessary to reselect the optimal cell to camp on.
  • the RSRP value obtained by the modem is -130dbm within 30 seconds, and the threshold set by the corresponding RSRP is -110dbm, it is considered that the network condition of the current serving cell is poor; or within 60 seconds, the modem The obtained SINR value is 5dbm, and the threshold value corresponding to the SINR is set to 10dbm, it is considered that the network condition of the current serving cell is poor; or within 30 seconds, the RSRQ value obtained by the modem is -10dbm, which corresponds to the RSRQ If the set threshold is -5dbm, it is considered that the current network condition of the serving cell is poor.
  • the network signal strength of the current serving cell is lower than the network signal strength threshold, which indicates that the current serving cell has poor network conditions, and needs to re-select a cell to camp in the candidate cell, and the selection of the camped cell is based on the candidate.
  • the target network characteristic value of the cell is determined.
  • neighboring cells that satisfy a preset wireless network type and whose network signal strength meets the first network signal strength are selected as candidate cells.
  • the target network characteristic value includes network signal strength, cell bandwidth value, sampling point value, and cell frequency point magnification value.
  • the target network characteristic value of the candidate cell is detected, and when the network condition of the current serving cell is poor, it automatically searches for new camping cells that meet the conditions. .
  • step 102 the target network quality of the candidate cell is determined according to the target network characteristic value of the candidate cell and the target coefficient of the target network characteristic value.
  • the network feature values of different dimensions have different degrees of influence on the network quality of the cell, so the target coefficients corresponding to the network feature values of different dimensions are also different.
  • the target coefficient corresponding to the network characteristic value in this dimension is larger, in which the network quality is reflected in the data transmission rate of the cell, and the better the network quality, the faster the data transmission rate.
  • step 102 includes: A. The absolute value of the product between the target network feature value of each dimension in the candidate cell and the target coefficient of the dimension is used as the initial result of the dimension; B 2. Sum the initial results in multiple dimensions to obtain the target network quality of the candidate cell.
  • the target network quality of the candidate cell is determined, which lays a data foundation for the subsequent selection of the target candidate cell according to the target network quality.
  • a target candidate cell is selected from the candidate cells according to the target network quality of the candidate cell.
  • the candidate cell with the highest target network quality is used as the target candidate cell.
  • the target candidate cell By selecting the target candidate cell from the candidate cells according to the target network quality of the candidate cell, it is ensured that the network condition of the target candidate cell is optimal among the candidate cells.
  • a resident cell is selected according to the network signal strength of the current serving cell and the network signal strength of the target candidate cell.
  • the target candidate cell is the current optimal cell needs to compare the network signal strength of the target candidate cell with the network signal strength of the current serving cell.
  • the target candidate cell meets a preset condition, the target candidate cell is As the current optimal cell, the cell chooses to camp on the target candidate cell, and if the target candidate cell does not meet the conditions, it chooses to stay on the current serving cell.
  • the target candidate cell when the difference between the network signal strength of the target candidate cell and the network signal strength of the current serving cell is greater than the second network signal strength, the target candidate cell is camped;
  • the difference between the network signal strength of the target candidate cell and the network signal strength of the current serving cell is less than or equal to the second network signal strength, camping on the current serving cell and re-detecting the target network characteristic value of the candidate cell, Until a new resident target candidate cell is determined according to the target network characteristic value of the re-detected candidate cell.
  • a second network signal strength is preset, and a difference between the network signal strength of the target candidate cell and the obtained network signal strength of the current serving cell is calculated. If the difference is greater than the second network signal strength, the target The candidate cell is the current optimal cell.
  • the signal strength of the second network is 6dbm
  • the obtained RSRP value of the current serving cell is -115dbm
  • the obtained target candidate cell RSRP value is -105dbm
  • the difference between the latter and the former is 10dbm
  • 10dbm is greater than 6dbm
  • the target candidate cell is the current optimal cell
  • the difference is less than the signal strength of the second network
  • the current serving cell will continue to reside, and a new round of candidate cell targets will be performed according to a preset period
  • the network characteristic value measurement detects whether there is a target candidate cell that meets the conditions, and if so, the new target candidate cell is used as the optimal cell.
  • the modem in the device releases the current wireless resource control connection and wireless resource locally, chooses the new optimal cell to camp on, and the non-access layer of the modem initiates a service request to resume the service in the new optimal cell.
  • the target network quality of the candidate cell is determined according to the target network characteristic value of the detected candidate cell and the target coefficient of the target network characteristic value, and then the optimal cell resides optimally according to the target network quality. It improves network detection efficiency, saves network maintenance time, and improves users' Internet experience.
  • a wireless network optimization macro switch is set.
  • the wireless network optimization macro switch when the wireless network optimization macro switch is turned on, the default user of the device needs the wireless network autonomous optimization function.
  • the network optimization of the current serving cell is autonomously performed, and target candidates are selected.
  • the application processor displays the current status of the device when the network quality of the current serving cell is detected to be poor.
  • a prompt box is displayed on the interface, for example: "The quality of the wireless network you are using is not good.
  • the corresponding options are “Yes” and “No” below the prompt box, of which "Yes”
  • the option is associated with the "Wireless Network Independent Optimization Function" macro control switch, and the next step is performed; when the user clicks the "No” option, the prompt box will not be repeatedly called before the device is offline, and the device will be restarted after offline. It can be called again when the network is located in the new community. In the case where it is detected that the user clicks "No" more than three times on the prompt box that is displayed multiple times, the prompt box is no longer called unless the user restarts the device.
  • the wireless network optimization macro switch is set to determine whether to enable the wireless network optimization function according to the user's different scenario requirements, which extends the application scenario of the terminal device.
  • FIG. 2 is a flowchart of a wireless network autonomous optimization method according to an embodiment of the present application. This embodiment provides an implementation manner for the foregoing embodiment. As shown in FIG. 2, the method includes steps 201 to 209.
  • step 201 among neighboring cells of the current serving cell, neighboring cells that satisfy a preset wireless network type and whose network signal strength meets the first network signal strength are selected as candidate cells.
  • the preset wireless network type indicates the frequency band range supported by the current wireless network
  • the neighboring cells meet the preset wireless network type, that is, the frequency band that the neighboring cell belongs to belongs to the frequency range supported by the current wireless network; correspondingly, The neighboring cell does not meet the preset wireless network type, which means that the frequency band to which the neighboring cell belongs is not in the frequency range supported by the current wireless network.
  • the adjacent cell is not considered as a candidate cell.
  • neighboring cells that satisfy a preset wireless network type and have a network signal strength greater than the first network signal strength are selected as candidate cells.
  • step 202 when the network signal strength of the current serving cell is lower than the network signal strength threshold, a target network characteristic value of the candidate cell is detected, where the target network characteristic value includes network signal strength, cell bandwidth value, sampling Point value and cell frequency magnification value.
  • the network signal strength includes RSRP, SINR, and RSRQ.
  • RSRP is used to measure the downlink coverage of the cell.
  • SINR is used to reflect the quality of the received signal.
  • RSRQ can reflect the relative size between the signal and interference.
  • RSRP The level of SINR and RSRQ will affect the demodulation problem of the terminal. Comprehensive consideration can fully reflect the quality of the cellular network. The larger the cell bandwidth value, the higher the upper bandwidth limit allocated to the cell terminal, and the higher the terminal peak rate. The more the number of sampling points, the greater the probability that the cell will be the primary coverage cell in the area, so it is considered as the characteristic value of the target network.
  • the cell frequency magnification value is the target network characteristic value.
  • a sample network characteristic value corresponding to a sample cell is obtained, where the sample network characteristic value includes a network signal strength, a cell bandwidth value, and a sampling point value.
  • the sample network characteristic value is directly measured by a technician using experience or related equipment in the sample cell.
  • a mobile terminal with normal communication is written into an engineering mode program, and then the network signal strength is generated.
  • the AT command is used to capture and sample the signaling information of the own cell, and the cell bandwidth value of the sample cell is determined according to the MIB message in the signaling information.
  • step 204 the sample network quality of the sample cell is determined according to the uplink average rate and the downlink average rate of the sample cell.
  • the sample network quality reflects the influence of the sample network characteristic value on the sample cell network condition, and different sample network characteristic values correspond to different sample network quality.
  • the sample network quality includes "good sample network quality” and “poor sample network quality”.
  • the quality of the sample network is obtained based on the average uplink and downlink rates of the sample cells.
  • the uplink average rate of the sample cell when the uplink average rate of the sample cell is greater than the first rate threshold and the downlink average rate is greater than the second rate threshold, it is determined that the sample network quality of the sample cell is good; In the case: the uplink average rate of the sample cell is less than or equal to the first rate threshold, and the downlink average rate is less than or equal to the second rate threshold, and it is determined that the sample network quality of the sample cell is poor.
  • step 205 a preset algorithm is used to train the sample network quality of the sample cell and the sample network characteristic values corresponding to the sample cell to obtain the target coefficient of the network signal strength, the target coefficient of the cell bandwidth value, and the value of the sampling point. Target coefficient.
  • the preset algorithm includes at least one of a decision tree algorithm and a classification model algorithm.
  • the sample network quality of the sample cells and the sample network characteristic values corresponding to the sample cells are first mapped to form nominal data, as shown in the following table:
  • g (D, X) H (D) -H (D
  • the information gain values of the three characteristics of the network signal strength, cell bandwidth value, and sampling point value are obtained, and normalized processing is performed to obtain the network.
  • Target coefficients for signal strength, target coefficients for cell bandwidth values, and target coefficients for sample point values are obtained.
  • step 206 the cell frequency point information of the candidate cell is obtained, and according to the correspondence relationship between the cell frequency point information and the preset coefficient, the preset coefficient corresponding to the cell frequency point information of the candidate cell is used as the target coefficient.
  • the target coefficient of the cell frequency magnification value is described.
  • a terminal device with normal network communication in the candidate cell is selected, such as a mobile phone, a tablet computer, or a notebook computer, and the frequency information of the candidate cell is obtained using the frequency point information acquisition software pre-installed in the terminal device.
  • a person skilled in the art has preset corresponding coefficients for each cell frequency point information according to actual experience. The larger the coefficient, the better the user perception of the cell corresponding to the cell frequency point information, and the sum of all preset coefficients is equal to Preset total coefficients. For example, if the preset total coefficient is 1, the cell frequency point information includes frequency point A, frequency point B, frequency point C, frequency point D, frequency point E, and frequency point F.
  • the preset coefficient corresponding to frequency point B is K2
  • K2 is taken as The target coefficient of the cell frequency magnification value.
  • step 207 the absolute value of the product between the target network feature value of each dimension in the candidate cell and the target coefficient of the dimension is used as the initial result of the dimension, and the initial results of multiple dimensions are summed. Get the target network quality of the candidate cell.
  • the target network feature value has multiple dimensions, and each dimension corresponds to a target coefficient.
  • the network signal strength of candidate cell A RSRP is x 1
  • SINR is x 2
  • RSRQ is x 3
  • cell bandwidth value is x 4
  • sampling point value is x 5
  • RSRP target coefficient is a 1
  • the target coefficient of the SINR is a 2
  • the target coefficient of the RSRQ is a 3
  • the target coefficient of the cell bandwidth value is a 4
  • the target coefficient of the sampling point value is a 5
  • the target coefficient of the cell frequency point magnification value is a 6 .
  • the target network quality of the candidate cell is:
  • k is the value of the cell frequency magnification.
  • the target candidate cell is selected from the candidate cells according to the target network quality of the candidate cell.
  • the target network quality of the candidate cells is sorted from high to low, and the candidate cell with the highest target network quality is selected as the target candidate cell.
  • a prompt box is displayed on the current display interface of the device, for example, "the current serving cell is already the optimal cell.”
  • a camping cell is selected according to the network signal strength of the current serving cell and the network signal strength of the target candidate cell.
  • the target of the network signal strength target coefficient and the cell bandwidth value is obtained through training.
  • the coefficient and the target coefficient of the sampling point value are combined with the preset coefficient value to obtain the target coefficient of the cell frequency point magnification value.
  • a candidate cell is obtained according to the obtained target network feature value of each dimension and the target coefficient of the dimension.
  • the target network quality of the community where the optimal community resides based on merit, improves network detection efficiency, saves network maintenance time, and improves the user's Internet experience.
  • FIG. 3 is a schematic structural diagram of a wireless network autonomous optimization device provided by an embodiment of the present application, which can execute a wireless network autonomous optimization method provided by any embodiment of the present application.
  • the device includes a target network characteristic value detection module 31, a target network quality determination module 32, a target candidate cell selection module 33, and a cell camping module 34.
  • the target network characteristic value detection module 31 is configured to detect a target network characteristic value of a candidate cell when a network signal strength of a current serving cell is lower than a network signal strength threshold, wherein the target network characteristic value includes a network signal Intensity, cell bandwidth value, sampling point value and cell frequency point magnification value.
  • the target network quality determination module 32 is configured to determine the target network quality of the candidate cell according to the target network characteristic value of the candidate cell and the target coefficient of the target network characteristic value.
  • the target candidate cell selection module 33 is configured to select the target candidate cell from the candidate cells according to the target network quality of the candidate cell.
  • the cell camping module 34 is configured to select a camping cell according to the network signal strength of the current serving cell and the network signal strength of the target candidate cell.
  • the apparatus further includes a candidate cell determination module, configured to:
  • the network signal strength includes at least one of a reference signal received power parameter, a reference signal received quality parameter, and a signal to interference plus noise ratio parameter.
  • the target network quality determination module 32 is set to:
  • the initial results of multiple dimensions are summed to obtain the target network quality of the candidate cell.
  • the device further includes a target coefficient determination module, configured to:
  • sample network characteristic value corresponding to a sample cell, where the sample network characteristic value includes a network signal strength, a cell bandwidth value, and a sampling point value;
  • the preset network is used to train the sample network quality of the sample cell and the sample network feature value corresponding to the sample cell to obtain the target coefficient of the target network feature value.
  • the preset algorithm includes a decision tree algorithm and a classification model algorithm. At least one of.
  • the target coefficient determining module further includes a cell frequency point magnification value target coefficient determining unit, configured to: obtain cell frequency point information of the candidate cell; and according to the cell frequency point information and a preset The corresponding relationship of the coefficients uses a preset coefficient corresponding to the cell frequency point information of the candidate cell as the target coefficient of the cell frequency point magnification value.
  • the target coefficient determination module is further configured to determine the sample cell when the uplink average rate of the sample cell is greater than the first rate threshold and the downlink average rate is greater than the second rate threshold.
  • the quality of the sample network is good; if one of the following conditions is met: the average uplink rate of the sample cell is less than or equal to the first rate threshold, and the average downlink rate is less than or equal to the second rate threshold, and the sample network quality of the sample cell is determined Is bad.
  • the cell camping module 34 is configured to: when the difference between the network signal strength of the target candidate cell and the network signal strength of the current serving cell is greater than the second network signal strength And camping on the target candidate cell; if the difference between the network signal strength of the target candidate cell and the network signal strength of the current serving cell is less than or equal to the signal strength of the second network, camping on the current serving cell, and The target network characteristic value of the candidate cell is re-detected until a new resident target candidate cell is determined according to the target network characteristic value of the re-detected candidate cell.
  • An apparatus for autonomously optimizing a wireless network provided by an embodiment of the present application may execute a method for autonomously optimizing a wireless network provided by any embodiment of the present application.
  • a method for autonomously optimizing a wireless network provided by any embodiment of the present application may execute a method for autonomously optimizing a wireless network provided by any embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of a device according to an embodiment of the present application.
  • FIG. 4 shows a block diagram of an exemplary device 400 suitable for use in implementing embodiments of the present application.
  • the device 400 shown in FIG. 4 is only an example, and should not impose any limitation on the functions and scope of use of the embodiments of the present application.
  • the device 400 is represented in the form of a general-purpose computing device.
  • the components of the device 400 may include, but are not limited to, at least one processor or at least one processing unit 401, a system memory 402, and a bus 403 connecting different system components (including the system memory 402 and the processing unit 401).
  • the bus 403 represents at least one of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local area bus using any of a variety of bus structures.
  • these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the enhanced ISA bus, and the Video Electronics Standards Association Association (VESA) local area bus and Peripheral Component Interconnect (PCI) bus.
  • Device 400 typically includes a variety of computer system-readable media. These media can be any available media that can be accessed by the device 400, including volatile and non-volatile media, removable and non-removable media.
  • the system memory 402 may include a computer system readable medium in the form of volatile memory, such as Random Access Memory (RAM) 404 and / or cache memory 405.
  • RAM Random Access Memory
  • the device 400 may include other removable / non-removable, volatile / nonvolatile computer system storage media.
  • the storage system 406 may provide a hard disk drive for reading and writing non-removable, non-volatile magnetic media (not shown in FIG. 4).
  • a disk drive may be provided for reading and writing to a removable non-volatile disk (for example, a "floppy disk"), and a removable non-volatile disk (for example, a compact disk Disc-Read (Only Memory, CD-ROM), Digital Video Disc (Read-Only Memory, DVD-ROM) or other optical media).
  • each drive can be connected to the bus 403 through at least one data medium interface.
  • the memory 402 may include at least one program product having a set (for example, at least one) of program modules configured to perform the functions of the embodiments of the present application.
  • a program / utility tool 408 having a set (at least one) of program modules 407 may be stored in, for example, the memory 402.
  • Such program modules 407 include, but are not limited to, an operating system, at least one application program, other program modules, and program data. These Each or some combination of examples may include an implementation of a network environment.
  • the program module 407 generally performs functions and / or methods in the embodiments described in this application.
  • the device 400 may also communicate with at least one external device 409 (eg, a keyboard, pointing device, display 410, etc.), may also communicate with at least one device that enables a user to interact with the device 400, and / or with the device 400 that enables the device 400 to communicate with Any device that communicates with at least one other computing device (eg, network card, modem, etc.) communicates. Such communication can be performed through an input / output (I / O) interface 411. Moreover, the device 400 may also communicate with at least one network (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 412.
  • LAN local area network
  • WAN wide area network
  • public network such as the Internet
  • the network adapter 412 communicates with other modules of the device 400 through the bus 403. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in conjunction with the device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, and disk arrays (Redundant Arrays) Independent Disks (RAID) systems, tape drives, and data backup storage systems.
  • the processing unit 401 executes various functional applications and data processing by running programs stored in the system memory 402.
  • the method for autonomously optimizing a wireless network includes: low network signal strength in a current serving cell
  • a target network characteristic value of a candidate cell is detected, where the target network characteristic value includes a network signal strength, a cell bandwidth value, a sampling point value, and a cell frequency point magnification value;
  • the target network characteristic value of the candidate cell and the target coefficient of the target network characteristic value are used to determine the target network quality of the candidate cell; and the target candidate cell is selected from the candidate cell according to the target network quality of the candidate cell.
  • Cell selecting a resident cell based on the network signal strength of the current serving cell and the network signal strength of the target candidate cell.
  • An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-executable instructions, when executed by a computer processor, implement a method for autonomously optimizing a wireless network, including: low network signal strength in a current serving cell
  • a target network characteristic value of a candidate cell is detected, where the target network characteristic value includes a network signal strength, a cell bandwidth value, a sampling point value, and a cell frequency point magnification value;
  • the target network characteristic value of the candidate cell and the target coefficient of the target network characteristic value are used to determine the target network quality of the candidate cell; and the target candidate cell is selected from the candidate cell according to the target network quality of the candidate cell.
  • Cell selecting a resident cell based on the network signal strength of the current serving cell and the network signal strength of the target candidate cell.
  • a storage medium containing computer-executable instructions provided in the embodiments of the present application is not limited to the method operations described above, and may also implement a wireless network autonomous provided by any embodiment of the present application. Relevant operations in optimization methods.
  • the computer-readable storage medium in the embodiments of the present application may adopt any combination of at least one computer-readable medium.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in combination with an instruction execution system, apparatus, or device.
  • the computer-readable signal medium may include a data signal in baseband or propagated as part of a carrier wave, which carries a computer-readable program code. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable medium may send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device .
  • the program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the foregoing.
  • any appropriate medium including but not limited to wireless, wire, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the foregoing.
  • the computer program code for performing the operations of this application may be written in at least one programming language or a combination thereof, the programming language including an object-oriented programming language—such as Java, Smalltalk, C ++, and also conventional procedural Programming language—such as "C" or a similar programming language.
  • the program code can be executed entirely on the user's computer, partly on the user's computer, as an independent software package, partly on the user's computer, partly on a remote computer, or entirely on a remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider) Internet connection).
  • LAN local area network
  • WAN wide area network
  • Internet service provider Internet service provider

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Abstract

本申请实施例公开了一种无线网络自主优化方法、装置、设备和介质。所述方法包括：在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值；根据候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量；根据候选蜂窝小区的目标网络质量，从候选蜂窝小区中选择目标候选蜂窝小区；根据当前服务小区的网络信号强度和目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。

Description

无线网络自主优化方法、装置、设备和介质
本申请要求在2018年9月29日提交中国专利局、申请号为201811147365.1的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本申请实施例涉及计算机技术领域，例如一种无线网络自主优化方法、装置、设备和介质。
背景技术
一个无线覆盖的区域，通常有很多蜂窝小区提供服务。特别是在当前流行的4G无线接入网络技术，一个无线覆盖区域，通常有很多同频段的、不同频段的、不同无线接入技术的蜂窝小区重叠覆盖。但有时候，在终端在这些多信号重叠的区域驻留，并获取网络服务的情况下，由于网络移动性参数设置不当，或者网络邻区漏配等情况，导致终端一直占用差蜂窝小区获取服务，用户在这种情况体验上网业务时，会明显觉察到网页打不开，上网慢，玩游戏卡顿等情况。
目前的无线网络优化主要由无线网络工程师来进行人工优化，在收到用户优化请求后，无线网络工程师通过对已运行的网络进行话务数据分析、现场测试数据采集、参数分析及硬件检查等手段，找出影响网络质量的原因，并且通过参数的修改、网络结构的调整、设备配置的调整和其它技术手段，确保系统能够正常运行。
但是，通过无线网络工程师进行人工优化，效率低下，多数情况下不能及时发现问题并且解决问题，使得用户长时间处于网络质量差的蜂窝小区，影响上网效率。
发明内容
本申请实施例提供一种无线网络自主优化方法、装置、设备和介质，以避免相关技术中通过人工方法对无线网络进行优化，伴随的检测效率低，维修时间长，用户上网体验差的情况。
第一方面，本申请实施例提供了一种无线网络自主优化方法，所述方法包 括：在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值，其中，所述目标网络特征值包括网络信号强度、小区带宽值、采样点数值以及小区频点放大倍数值；根据所述候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量；根据所述候选蜂窝小区的目标网络质量，从所述候选蜂窝小区中选择目标候选蜂窝小区；根据所述当前服务小区的网络信号强度和所述目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。
第二方面，本申请实施例提供了一种无线网络自主优化装置，所述装置包括目标网络特征值检测模块、目标网络质量确定模块、目标候选蜂窝小区选择模块及蜂窝小区驻留模块。目标网络特征值检测模块，设置为在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值，其中所述目标网络特征值包括网络信号强度、小区带宽值、采样点数值以及小区频点放大倍数值；目标网络质量确定模块，设置为根据所述候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量；目标候选蜂窝小区选择模块，设置为根据所述候选蜂窝小区的目标网络质量，从所述候选蜂窝小区中选择目标候选蜂窝小区；蜂窝小区驻留模块，设置为根据所述当前服务小区的网络信号强度和所述目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。
第三方面，本申请实施例提供了一种设备包括：至少一个处理器；存储装置，设置为存储至少一个程序，当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如本申请任一实施例中所述的一种无线网络自主优化方法。
第四方面，本申请实施例提供了一种计算机可读介质，其上存储有计算机程序，该计算机程序被处理器执行时，实现如本申请任一实施例中所述的无线网络自主优化方法。
附图说明
图1为本申请一实施例提供的一种无线网络自主优化方法的流程图；
图2为本申请一实施例提供的另一种无线网络自主优化方法的流程图；
图3为本申请一实施例提供的一种无线网络自主优化装置的结构示意图；
图4为本申请一实施例提供的一种设备的结构示意图。
具体实施方式
图1为本申请一实施例提供的一种无线网络自主优化方法的流程图，本实施例可适用于用户所处当前服务小区网络信号强度差的情况，该方法可以由本申请实施例提供的无线网络自主优化装置来执行，该装置可集成于无线路由器、移动终端(例如手机、智能手机、平板电脑)和固定终端(例如，台式电脑或笔记本电脑)中，如图1所示，该方法包括步骤101至步骤104。
在步骤101中，在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值。
其中，当前服务小区是指用户使用的终端或路由器当前所处的服务蜂窝小区，网络线号强度包括但不限于以下至少一项参数：参考信号接收功率(Reference Signal Receiving Power，RSRP)参数、信号与干扰加噪声比(Signal to Interference plus Noise Ratio，SINR)参数、参考信号接收质量(Reference Signal Receiving Quality，RSRQ)参数等。网络信号强度由设备中的调制解调器获取，根据检测的当前服务小区的网络信号强度来分析当前服务小区网络状况的好坏，如果当前服务小区网络信号强度高于或等于网络信号强度门限值，则认为当前服务小区网络状况好，继续驻留在当前服务小区；如果当前服务小区网络信号强度低于网络信号强度门限值，则认为当前服务小区网络状况差，需要重新选择最优小区驻留。例如，在30秒的时间内，调制解调器获取到的RSRP值为-130dbm，而对应RSRP设定的门限值为-110dbm，则认定当前服务小区网络状况差；或者在60秒的时间内，调制解调器获取到的SINR值为5dbm，而对应SINR设定的门限值为10dbm，则认定当前服务小区网络状况差；或者在30秒的时间内，调制解调器获取到的RSRQ值为-10dbm，而对应RSRQ设定的门限值为-5dbm，则认定当前服务小区网络状况差。
在一实施例中，当前服务小区的网络信号强度低于网络信号强度门限值，说明当前服务小区网络状况差，需要在候选蜂窝小区中重新选择小区驻留，而选择驻留小区是依据候选蜂窝小区的目标网络特征值确定的。
在一实施例中，将当前服务小区的相邻蜂窝小区中满足预设无线网络类型，且网络信号强度满足第一网络信号强度的相邻蜂窝小区，作为候选蜂窝小区。
在一实施例中，目标网络特征值包括网络信号强度、小区带宽值、采样点数值以及小区频点放大倍数值。
在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值，在当前服务小区网络状况差的情况下，自动搜索符合条件的新驻留小区。
在步骤102中，根据候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量。
在一实施例中，不同维度的网络特征值对蜂窝小区的网络质量有不同的影响程度，因此不同维度的网络特征值对应的目标系数也不同，网络特征值对网络质量的影响力越大，该维度网络特征值对应的目标系数就越大，其中，网络质量体现在蜂窝小区的数据传输速率上，网络质量越好则数据传输速率越快。
目标网络质量是根据目标网络特征值以及对应的目标系数计算得到的，目标网络特征值具有多个维度，每个维度对应一个目标系数，为了保证目标网络质量的结果与每个目标网络特征值正相关，因此，在一实施例中，步骤102包括：A、将候选蜂窝小区中每一个维度的目标网络特征值与该维度的目标系数之间乘积的绝对值，作为该维度的初始结果；B、对多个维度的初始结果进行求和，得到候选蜂窝小区的目标网络质量。
通过根据候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量，为后续根据目标网络质量选择目标候选蜂窝小区奠定了数据基础。
在步骤103中，根据候选蜂窝小区的目标网络质量，从候选蜂窝小区中选择目标候选蜂窝小区。
在一实施例中，将目标网络质量最高的候选蜂窝小区，作为目标候选蜂窝小区。
通过根据候选蜂窝小区的目标网络质量，从候选蜂窝小区中选择目标候选蜂窝小区，保证了目标候选蜂窝小区的网络状况在候选蜂窝小区中最优。
在步骤104中，根据当前服务小区的网络信号强度和目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。
其中，目标候选蜂窝小区是否为当前最优小区，需要将目标候选蜂窝小区的网络信号强度与当前服务小区的网络信号强度进行比较，在目标候选蜂窝小区满足预设条件的情况下，将目标候选蜂窝小区作为当前最优小区，选择驻留目标候选蜂窝小区，在目标候选蜂窝小区不满足条件的情况下，选择继续驻留当前服务小区。
在一实施例中，在目标候选蜂窝小区的网络信号强度与当前服务小区的网络信号强度的差值大于第二网络信号强度的情况下，驻留目标候选蜂窝小区；
在目标候选蜂窝小区的网络信号强度与当前服务小区的网络信号强度的差值小于或者等于第二网络信号强度的情况下，驻留当前服务小区，并重新检测候选蜂窝小区的目标网络特征值，直到根据重新检测的候选蜂窝小区的目标网络特征值确定新的驻留目标候选蜂窝小区为止。
在一实施例中，预设一个第二网络信号强度，将目标候选蜂窝小区的网络信号强度与获取的当前服务小区的网络信号强度做差计算，如果差值大于第二网络信号强度，则目标候选蜂窝小区为当前最优小区，例如：第二网络信号强度为6dbm，获取的当前服务小区RSRP值为-115dbm，获取的目标候选蜂窝小区RSRP值为-105dbm，后者与前者的差值为10dbm，而10dbm大于6dbm，则目标候选蜂窝小区为当前最优小区；如果差值小于第二网络信号强度，则继续驻留当前服务小区，并按预设周期进行新一轮候选蜂窝小区的目标网络特征值测量，检测是否有符合条件的目标候选蜂窝小区，如果有则将新的目标候选蜂窝小区作为最优小区。确定新的最优小区后，设备中的调制解调器本地释放当前无线资源控制连接和无线资源，选择新的最优小区驻留，并且调制解调器的非接入层在新的最优小区发起服务请求恢复业务。
本申请实施例提供的技术方案，通过根据检测的候选蜂窝小区的目标网络特征值以及目标网络特征值的目标系数，确定候选蜂窝小区目标网络质量，进而根据目标网络质量择优驻留最优小区，提高了网络检测效率，节省网络维修时间，改善用户的上网体验。
在上述实施例的基础上，为了能更好地依照用户需求来优化无线网络，设置一个无线网络优化宏开关。
示例性的，在无线网络优化宏开关打开的情况下，设备默认用户需要无线网络自主优化功能，在检测到当前服务小区网络质量差的情况下，自主对当前服务小区进行网络优化，选择目标候选蜂窝小区；在无线网络优化宏开关关闭的情况下，为了能让用户及时知晓当前网络状态并选择目标候选蜂窝小区，在检测到当前服务小区网络质量差的情况下，应用处理器在设备当前显示界面显示提示框，例如：“您当前使用的无线网络质量不好，是否要打开无线网络自主优化功能？”，对应的在提示框下面有“是”和“否”的选项，其中“是”选项关联到“无线网络自主优化功能”宏控制开关，并进行下一步；在检测到用户点击“否” 选项的情况下，该提示框在设备脱网前不再重复调用，脱网后设备重新搜网驻留新小区时，可重新调用。在检测到用户在多次显示的提示框上点击“否”3次以上的情况下，除非用户重启设备，否则不再调用提示框。
本实施方式，通过设置无线网络优化宏开关，根据用户不同的场景需求决定是否启用无线网络优化功能，扩展了终端设备的应用场景。
图2为本申请一实施例提供的一种无线网络自主优化方法的流程图。本实施例为上述实施例提供了一种实现方式，如图2所示，该方法包括步骤201至步骤209。
在步骤201中，将当前服务小区的相邻蜂窝小区中满足预设无线网络类型，且网络信号强度满足第一网络信号强度的相邻蜂窝小区，作为候选蜂窝小区。
其中，预设无线网络类型表示当前无线网络所支持的频段范围，相邻蜂窝小区满足预设无线网络类型，即表示相邻蜂窝小区所属频段，属于当前无线网络所支持的频段范围；对应的，相邻蜂窝小区不满足预设无线网络类型，即表示相邻蜂窝小区所属频段，不属于当前无线网络所支持的频段范围。
同时，为了提高无线网络优化的效率，在相邻蜂窝小区的网络信号强度过小的情况下，不考虑该相邻蜂窝小区作为候选蜂窝小区。
因此，在一实施例中，将当前服务小区的相邻蜂窝小区中满足预设无线网络类型，且网络信号强度大于第一网络信号强度的相邻蜂窝小区，作为候选蜂窝小区。
在步骤202中，在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值，其中目标网络特征值包括网络信号强度、小区带宽值、采样点数值以及小区频点放大倍数值。
在一实施例中，网络信号强度包括RSRP、SINR和RSRQ，RSRP用来衡量蜂窝小区的下行覆盖范围，SINR用来反映接收信号的质量，RSRQ能反映出信号和干扰之间的相对大小，RSRP、SINR和RSRQ的高低都会影响终端解调方面的问题，综合考虑，能全面的反映蜂窝小区网络质量的好坏。小区带宽值越大，分配给蜂窝小区终端的带宽上限就越高，终端峰值速率就越高。采样点数值越多，证明该蜂窝小区在该区域作为主覆盖小区的可能性就越大，因此考虑其作为目标网络特征值。考虑某区域内某小区频点优先级较高，吸纳用户数较多，会出现小区信号好但是用户感知很差的现象，因此同样考虑小区频点放大倍数值为目标网络特征值。
在步骤203中，获取样本蜂窝小区对应的样本网络特征值，其中所述样本网络特征值包括网络信号强度、小区带宽值以及采样点数值。
在一实施例中，样本网络特征值是由技术人员利用经验或者相关设备，在样本蜂窝小区中直接测量得到的，例如将通信正常的移动终端写入工程模式程序，则生成关于网络信号强度的信息，又例如在通信正常的移动终端中，使用AT命令来抓取样本蜂窝小区的信令信息，并根据信令信息中的MIB消息，确定样本蜂窝小区的小区带宽值。
在步骤204中，根据样本蜂窝小区的上行平均速率和下行平均速率，确定样本蜂窝小区的样本网络质量。
其中，样本网络质量体现了样本网络特征值对样本蜂窝小区网络状况的影响，不同的样本网络特征值对应的样本网络质量不同。样本网络质量包括“样本网络质量好”以及“样本网络质量差”，样本网络质量的好坏是根据样本蜂窝小区的上行和下行平均速率得到的。
在一实施例中，在样本蜂窝小区的上行平均速率大于第一速率阈值，且下行平均速率大于第二速率阈值的情况下，判定样本蜂窝小区的样本网络质量为好；在满足以下之一的情况下：样本蜂窝小区的上行平均速率小于或者等于第一速率阈值，下行平均速率小于或者等于第二速率阈值，判定样本蜂窝小区的样本网络质量为差。
在步骤205中，采用预设算法对样本蜂窝小区的样本网络质量以及样本蜂窝小区对应的样本网络特征值进行训练，以得到网络信号强度的目标系数、小区带宽值的目标系数，以及采样点数值的目标系数。
其中，所述预设算法包括决策树算法以及分类模型算法中的至少一项。
示例性的，在预设算法为决策树算法的情况下，首先将样本蜂窝小区的样本网络质量以及样本蜂窝小区对应的样本网络特征值，进行对应形成标称型数据，如下表所示：
小区编号 网络信号强度 小区带宽值 采样点数值 样本网络质量
1 X1 Y1 Z1 样本网络质量好
2 X2 Y2 Z2 样本网络质量差
…… …… …… …… ……
再根据上述表格中的数据，结合公式
Figure PCTCN2019108742-appb-000001
计算样本网络质量的 信息熵，其中p k表示第k种样本网络质量的个数占总样本网络质量个数的比例，由于在本实施例中样本网络质量只包括“样本网络质量好”以及“样本网络质量差”两种，则上述公式可变为H(D)＝-p 1log 2p 1-p 2log 2p 2，其中p 1以及p 2分别表示“样本网络质量好”个数以及“样本网络质量差”个数占总样本网络质量个数的比例，例如总样本网络质量个数为16个，“样本网络质量好”为7个，“样本网络质量差”为9个，则样本网络质量的信息熵H(D)＝-p 1log 2p 1-p 2log 2p 2＝0.989。
接下利用公式：
Figure PCTCN2019108742-appb-000002
求网络信号强度、小区带宽值以及采样点数值三种特征的条件熵，其中x 1,x 2,x 3分别表示网络信号强度、小区带宽值以及采样点数值。
最终根据公式：g(D,X)＝H(D)-H(D|X)得到网络信号强度、小区带宽值以及采样点数值三种特征的信息增益值，并进行归一处理，得到网络信号强度的目标系数、小区带宽值的目标系数，以及采样点数值的目标系数。
示例性的，在预设算法为分类模型算法的情况下，过程可以包括：1)在获取的样本蜂窝小区的训练数据集D下，分别训练出三个SVM分类器，三个SVM分类器分别对应网络信号强度、小区带宽值以及采样点数值三种特征。2)测试三个SVM分类器的分类效果，并统计正确分类个数count i，i＝1,2,3分别表示三种特征的正确分类数。3)对2)中求出的count i做归一处理，即可得到网络信号强度的目标系数、小区带宽值的目标系数，以及采样点数值的目标系数：
Figure PCTCN2019108742-appb-000003
在步骤206中，获取所述候选蜂窝小区的小区频点信息，并根据小区频点信息与预设系数的对应关系，将所述候选蜂窝小区的小区频点信息对应的预设系数，作为所述小区频点放大倍数值的目标系数。
在一实施例中，选取候选蜂窝小区中网络通信正常的终端设备，例如手机、平板电脑或者笔记本电脑等，利用终端设备中预安装的频点信息获取软件来获取候选蜂窝小区的小区频点信息。本领域技术人员根据实际经验，对于每个小区频点信息都预设了对应的系数，系数越大则说明小区频点信息对应的蜂窝小区的用户感知越好，并且所有预设系数的总和等于预设总系数，例如，假设预设总系数为1，小区频点信息包括频点A、频点B、频点C、频点D、频点E和频点F共六个小区频点信息，每个小区频点信息对应的预设系数分别为K1、K2、 K3、K4、K5和K6，则K1+K2+K3+K4+K5+K6＝1。
示例性的，假设获取到候选蜂窝小区的小区频点信息为频点B，而在小区频点信息与预设系数的对应关系中，频点B对应的预设系数为K2，则将K2作为小区频点放大倍数值的目标系数。
在步骤207中，将候选蜂窝小区中每一个维度的目标网络特征值与该维度的目标系数之间乘积的绝对值，作为该维度的初始结果，并对多个维度的初始结果进行求和，得到候选蜂窝小区的目标网络质量。
其中，目标网络质量是以得分形式体现的。目标网络特征值具有多个维度，每个维度对应一个目标系数。
示例性的，候选蜂窝小区A的网络信号强度：RSRP为x 1，SINR为x 2，RSRQ为x 3，小区带宽值为x 4，采样点数值为x 5，RSRP的目标系数为a 1，SINR的目标系数为a 2，RSRQ的目标系数为a 3，小区带宽值的目标系数为a 4，采样点数值的目标系数为a 5，小区频点放大倍数值的目标系数为a 6。则候选蜂窝小区的目标网络质量为：|a 1x 1|+|a 2x 2|+|a 3x 3|+|a 4x 4|+|a 5x 5|+|a 6k|，其中k为小区频点放大倍数值。
在步骤208中，根据候选蜂窝小区的目标网络质量，从候选蜂窝小区中选择目标候选蜂窝小区。
在一实施例中，将候选蜂窝小区的目标网络质量从高到低进行排序，选取目标网络质量最高的候选蜂窝小区，作为目标候选蜂窝小区。
在一实施例中，在当前服务小区的目标网络质量，高于目标候选蜂窝小区的目标网络质量的情况下，在设备当前显示界面显示提示框，例如：“当前服务小区已为最优小区”。
在步骤209中，根据当前服务小区的网络信号强度和目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。
本申请实施例提供的技术方案，通过根据样本蜂窝小区的样本网络特征值、样本网络特征值的样本系数以及样本蜂窝小区的样本网络质量，训练得到网络信号强度的目标系数、小区带宽值的目标系数，以及采样点数值的目标系数，再结合预设系数值，得到小区频点放大倍数值的目标系数，最终根据得到的每一个维度的目标网络特征值与该维度的目标系数，得到候选蜂窝小区的目标网络质量，以择优驻留最优小区，提高了网络检测效率，节省网络维修时间，改善用户的上网体验。
图3为本申请一实施例提供的一种无线网络自主优化装置的结构示意图， 可执行本申请任一实施例所提供的一种无线网络自主优化方法。如图3所示，该装置包括目标网络特征值检测模块31、目标网络质量确定模块32、目标候选蜂窝小区选择模块33以及蜂窝小区驻留模块34。
目标网络特征值检测模块31，设置为在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值，其中所述目标网络特征值包括网络信号强度、小区带宽值、采样点数值以及小区频点放大倍数值。
目标网络质量确定模块32，设置为根据所述候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量。
目标候选蜂窝小区选择模块33，设置为根据所述候选蜂窝小区的目标网络质量，从所述候选蜂窝小区中选择目标候选蜂窝小区。
蜂窝小区驻留模块34，设置为根据所述当前服务小区的网络信号强度和所述目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。
在上述实施例的基础上，所述装置还包括候选蜂窝小区确定模块，设置为：
将当前服务小区的相邻蜂窝小区中满足预设无线网络类型，且网络信号强度满足第一网络信号强度的相邻蜂窝小区，作为候选蜂窝小区；
其中，所述网络信号强度包括参考信号接收功率参数、参考信号接收质量参数以及信号与干扰加噪声比参数中的至少一项。
在上述实施例的基础上，目标网络特征值具有多个维度，每个维度对应一个目标系数，因此所述目标网络质量确定模块32，设置为：
将候选蜂窝小区中每一个维度的目标网络特征值与该维度的目标系数之间乘积的绝对值，作为该维度的初始结果；
对多个维度的初始结果进行求和，得到候选蜂窝小区的目标网络质量。
在上述实施例的基础上，所述装置还包括目标系数确定模块，设置为：
获取样本蜂窝小区对应的样本网络特征值，其中所述样本网络特征值包括网络信号强度、小区带宽值以及采样点数值；
根据样本蜂窝小区的上行平均速率和下行平均速率，确定样本蜂窝小区的样本网络质量；
采用预设算法对样本蜂窝小区的样本网络质量以及样本蜂窝小区对应的样本网络特征值进行训练，以得到目标网络特征值的目标系数，其中，所述预设算法包括决策树算法以及分类模型算法中的至少一项。
在上述实施例的基础上，所述目标系数确定模块还包括小区频点放大倍数值目标系数确定单元，设置为：获取所述候选蜂窝小区的小区频点信息；根据小区频点信息与预设系数的对应关系，将所述候选蜂窝小区的小区频点信息对应的预设系数，作为所述小区频点放大倍数值的目标系数。
在上述实施例的基础上，所述目标系数确定模块，还设置为：在样本蜂窝小区的上行平均速率大于第一速率阈值，且下行平均速率大于第二速率阈值的情况下，判定样本蜂窝小区的样本网络质量为好；在满足以下之一的情况下：样本蜂窝小区的上行平均速率小于或者等于第一速率阈值，下行平均速率小于或者等于第二速率阈值，判定样本蜂窝小区的样本网络质量为差。
在上述实施例的基础上，所述蜂窝小区驻留模块34，设置为：在目标候选蜂窝小区的网络信号强度与所述当前服务小区的网络信号强度的差值大于第二网络信号强度的情况下，驻留目标候选蜂窝小区；在目标候选蜂窝小区的网络信号强度与所述当前服务小区的网络信号强度的差值小于或者等于第二网络信号强度的情况下，驻留当前服务小区，并重新检测候选蜂窝小区的目标网络特征值，直到根据重新检测的候选蜂窝小区的目标网络特征值确定新的驻留目标候选蜂窝小区为止。
本申请实施例所提供的一种无线网络自主优化装置，可执行本申请任一实施例所提供的一种无线网络自主优化方法。未在本实施例中详尽描述的技术细节，可参见本申请任一实施例提供的一种无线网络自主优化方法。
图4为本申请一实施例提供的一种设备的结构示意图。图4示出了适于用来实现本申请实施方式的示例性设备400的框图。图4显示的设备400仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。
如图4所示，设备400以通用计算设备的形式表现。设备400的组件可以包括但不限于：至少一个处理器或者至少一个处理单元401，系统存储器402，连接不同系统组件(包括系统存储器402和处理单元401)的总线403。
总线403表示几类总线结构中的至少一种，包括存储器总线或者存储器控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些体系结构包括但不限于工业标准体系结构(Industry Standard Architecture，ISA)总线，微通道体系结构(Micro Channel Architecture，MAC)总线，增强型ISA总线、视频电子标准协会(Video Electronics Standards Association，VESA)局域总线以及外围组件互连(Peripheral Component  Interconnect，PCI)总线。
设备400典型地包括多种计算机系统可读介质。这些介质可以是任何能够被设备400访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
系统存储器402可以包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(Random Access Memory，RAM)404和/或高速缓存存储器405。设备400可以包括其它可移动/不可移动的、易失性/非易失性计算机系统存储介质。仅作为举例，存储系统406可以提供用于读写不可移动的、非易失性磁介质的硬盘驱动器(图4未显示)。尽管图4中未示出，可以提供用于对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及可以提供对可移动非易失性光盘(例如只读光盘(Compact Disc-Read Only Memory，CD-ROM),数字视盘(Digital Video Disc-Read Only Memory，DVD-ROM)或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过至少一个数据介质接口与总线403相连。存储器402可以包括至少一个程序产品，该程序产品具有一组(例如至少一个)程序模块，这些程序模块被配置以执行本申请各实施例的功能。
具有一组(至少一个)程序模块407的程序/实用工具408，可以存储在例如存储器402中，这样的程序模块407包括但不限于操作系统、至少一个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。程序模块407通常执行本申请所描述的实施例中的功能和/或方法。
设备400也可以与至少一个外部设备409(例如键盘、指向设备、显示器410等)通信，还可与至少一个使得用户能与该设备400交互的设备通信，和/或与使得该设备400能与至少一个其它计算设备进行通信的任何设备(例如网卡，调制解调器等等)通信。这种通信可以通过输入/输出(Input/Output，I/O)接口411进行。并且，设备400还可以通过网络适配器412与至少一个网络(例如局域网(Local Area Network，LAN)，广域网(Wide Area Network，WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器412通过总线403与设备400的其它模块通信。应当明白，尽管图中未示出，可以结合设备400使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、磁盘阵列(Redundant Arrays of Independent Disks，RAID)系统、磁带驱动器以及数据备份存储系统等。
处理单元401通过运行存储在系统存储器402中的程序，从而执行各种功能应用以及数据处理，例如实现本申请实施例所提供的无线网络自主优化方法，包括：在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值，其中所述目标网络特征值包括网络信号强度、小区带宽值、采样点数值以及小区频点放大倍数值；根据所述候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量；根据所述候选蜂窝小区的目标网络质量，从所述候选蜂窝小区中选择目标候选蜂窝小区；根据所述当前服务小区的网络信号强度和所述目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。
本申请一实施例还提供了一种计算机可读存储介质，所述计算机可执行指令在由计算机处理器执行时，实现一种无线网络自主优化方法，包括：在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值，其中所述目标网络特征值包括网络信号强度、小区带宽值、采样点数值以及小区频点放大倍数值；根据所述候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量；根据所述候选蜂窝小区的目标网络质量，从所述候选蜂窝小区中选择目标候选蜂窝小区；根据所述当前服务小区的网络信号强度和所述目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。
当然，本申请实施例所提供的一种包含计算机可执行指令的存储介质，其计算机可执行指令不限于如上所述的方法操作，还可以执行本申请任意实施例所提供的一种无线网络自主优化方法中的相关操作。本申请实施例的计算机可读存储介质，可以采用至少一个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有至少一个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括——但不限于无线、电线、光缆、射频(Radio Frequency，RF)等等，或者上述的任意合适的组合。
可以以至少一种程序设计语言或其组合来编写用于执行本申请操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。

Claims (10)

  1. 一种无线网络自主优化方法，包括：
    在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值，其中所述目标网络特征值包括网络信号强度、小区带宽值、采样点数值以及小区频点放大倍数值；
    根据所述候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量；
    根据所述候选蜂窝小区的目标网络质量，从所述候选蜂窝小区中选择目标候选蜂窝小区；
    根据所述当前服务小区的网络信号强度和所述目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。
  2. 根据权利要求1所述的方法，在检测候选蜂窝小区的目标网络特征值之前，还包括：
    将当前服务小区的相邻蜂窝小区中满足预设无线网络类型，且网络信号强度满足第一网络信号强度的相邻蜂窝小区，作为候选蜂窝小区；
    其中，所述网络信号强度包括参考信号接收功率参数、参考信号接收质量参数以及信号与干扰加噪声比参数中的至少一项。
  3. 根据权利要求1所述的方法，其中，所述目标网络特征值具有多个维度，每个维度对应一个目标系数，所述根据所述候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量，包括：
    将候选蜂窝小区中每一个维度的目标网络特征值与该维度的目标系数之间乘积的绝对值，作为该维度的初始结果；
    对多个维度的初始结果进行求和，得到候选蜂窝小区的目标网络质量。
  4. 根据权利要求1所述的方法，根据所述候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量之前，还包括：
    获取样本蜂窝小区对应的样本网络特征值，其中所述样本网络特征值包括网络信号强度、小区带宽值以及采样点数值；
    根据样本蜂窝小区的上行平均速率和下行平均速率，确定样本蜂窝小区的样本网络质量；
    采用预设算法对样本蜂窝小区的样本网络质量以及样本蜂窝小区对应的样本网络特征值进行训练，以得到目标网络特征值的目标系数，其中，所述预设 算法包括决策树算法以及分类模型算法中的至少一项。
  5. 根据权利要求4所述的方法，其中，根据样本蜂窝小区的上行平均速率和下行平均速率，确定样本蜂窝小区的样本网络质量，包括：
    在样本蜂窝小区的上行平均速率大于第一速率阈值，且下行平均速率大于第二速率阈值的情况下，判定样本蜂窝小区的样本网络质量为好；在满足以下之一的情况下：样本蜂窝小区的上行平均速率小于或者等于第一速率阈值，下行平均速率小于或者等于第二速率阈值，判定样本蜂窝小区的样本网络质量为差。
  6. 根据权利要求4所述的方法，采用预设算法对样本蜂窝小区的样本网络质量以及样本蜂窝小区对应的样本网络特征值进行训练之后，还包括：
    获取所述候选蜂窝小区的小区频点信息；
    根据小区频点信息与预设系数的对应关系，将所述候选蜂窝小区的小区频点信息对应的预设系数，作为所述小区频点放大倍数值的目标系数。
  7. 根据权利要求1所述的方法，其中，根据所述当前服务小区的网络信号强度和所述目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区，包括：
    在目标候选蜂窝小区的网络信号强度与所述当前服务小区的网络信号强度的差值大于第二网络信号强度的情况下，驻留目标候选蜂窝小区；
    在目标候选蜂窝小区的网络信号强度与所述当前服务小区的网络信号强度的差值小于或者等于第二网络信号强度的情况下，驻留当前服务小区，并重新检测候选蜂窝小区的目标网络特征值，直到根据重新检测的候选蜂窝小区的目标网络特征值确定新的驻留目标候选蜂窝小区为止。
  8. 一种无线网络自主优化装置，包括目标网络特征值检测模块、目标网络质量确定模块、目标候选蜂窝小区选择模块及蜂窝小区驻留模块；
    目标网络特征值检测模块，设置为在当前服务小区的网络信号强度低于网络信号强度门限值的情况下，检测候选蜂窝小区的目标网络特征值，其中所述目标网络特征值包括网络信号强度、小区带宽值、采样点数值以及小区频点放大倍数值；
    目标网络质量确定模块，设置为根据所述候选蜂窝小区的目标网络特征值，以及目标网络特征值的目标系数，确定候选蜂窝小区的目标网络质量；
    目标候选蜂窝小区选择模块，设置为根据所述候选蜂窝小区的目标网络质量，从所述候选蜂窝小区中选择目标候选蜂窝小区；
    蜂窝小区驻留模块，设置为根据所述当前服务小区的网络信号强度和所述目标候选蜂窝小区的网络信号强度，选择驻留蜂窝小区。
  9. 一种设备，包括：
    至少一个处理器；
    存储装置，设置为存储至少一个程序，
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器，实现如权利要求1-7中任一项所述的无线网络自主优化方法。
  10. 一种计算机可读介质，其上存储有计算机程序，所述计算机程序被处理器执行时，实现如权利要求1-7中任一项所述的无线网络自主优化方法。
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112637801A (zh) * 2020-12-11 2021-04-09 深圳市汇顶科技股份有限公司 一种小区驻留方法、终端及计算机可读存储介质
CN113473502A (zh) * 2020-08-27 2021-10-01 几维通信技术(深圳)有限公司 用于自动化网络优化的终端设备、处理系统及优化方法
CN114189297A (zh) * 2021-11-23 2022-03-15 上海移为通信技术股份有限公司 无线通信设备及其干扰检测方法
CN114363945A (zh) * 2021-12-22 2022-04-15 苏州迈科网络安全技术股份有限公司 基于网络拓扑的Wifi质量得分计算方法、装置、终端及存储介质
CN114390563A (zh) * 2021-12-30 2022-04-22 中国电信股份有限公司 一种5g小区驻留能力评估方法、装置及电子设备
CN114867075A (zh) * 2022-06-09 2022-08-05 西安紫光展锐科技有限公司 无线网络切换方法、装置、设备、存储介质及产品
CN116614187A (zh) * 2023-07-05 2023-08-18 广州市梦享网络技术有限公司 一种信号强度确定方法、装置、设备及存储介质
CN117412315A (zh) * 2023-12-12 2024-01-16 深圳通诚无限科技有限公司 一种基于数据分析的无线通讯网络数据优化方法

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109121149B (zh) * 2018-09-29 2021-12-28 闻泰通讯股份有限公司 无线网络自主优化方法、装置、设备和存储介质
CN111432445B (zh) * 2020-04-02 2022-07-12 闻泰通讯股份有限公司 网络小区的选择方法、装置、移动终端和存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008054800A2 (en) * 2006-10-31 2008-05-08 Telcordia Technologies, Inc. Dynamic network selection using kernels
CN103476080A (zh) * 2013-09-10 2013-12-25 镇江青思网络科技有限公司 基于滞留时间的自适应垂直切换方法
CN107071841A (zh) * 2017-03-02 2017-08-18 重庆邮电大学 异构网络中基于动态权重优化的垂直切换方法
CN107333293A (zh) * 2017-07-28 2017-11-07 广东欧珀移动通信有限公司 测量信息上报方法及装置
CN109121149A (zh) * 2018-09-29 2019-01-01 闻泰通讯股份有限公司 无线网络自主优化方法、装置、设备和存储介质

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1277421C (zh) * 2003-01-14 2006-09-27 华为技术有限公司 一种多频段小区选择的实现方法
CN101854690A (zh) * 2009-03-31 2010-10-06 北京三星通信技术研究有限公司 小区重选方法及其双模终端
CN107241721A (zh) * 2016-03-29 2017-10-10 努比亚技术有限公司 移动终端网络重选控制方法及装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008054800A2 (en) * 2006-10-31 2008-05-08 Telcordia Technologies, Inc. Dynamic network selection using kernels
CN103476080A (zh) * 2013-09-10 2013-12-25 镇江青思网络科技有限公司 基于滞留时间的自适应垂直切换方法
CN107071841A (zh) * 2017-03-02 2017-08-18 重庆邮电大学 异构网络中基于动态权重优化的垂直切换方法
CN107333293A (zh) * 2017-07-28 2017-11-07 广东欧珀移动通信有限公司 测量信息上报方法及装置
CN109121149A (zh) * 2018-09-29 2019-01-01 闻泰通讯股份有限公司 无线网络自主优化方法、装置、设备和存储介质

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113473502A (zh) * 2020-08-27 2021-10-01 几维通信技术(深圳)有限公司 用于自动化网络优化的终端设备、处理系统及优化方法
CN113473502B (zh) * 2020-08-27 2024-02-02 几维通信技术(深圳)有限公司 用于自动化网络优化的终端设备、处理系统及优化方法
CN112637801B (zh) * 2020-12-11 2023-05-23 深圳市汇顶科技股份有限公司 一种小区驻留方法、终端及计算机可读存储介质
CN112637801A (zh) * 2020-12-11 2021-04-09 深圳市汇顶科技股份有限公司 一种小区驻留方法、终端及计算机可读存储介质
CN114189297A (zh) * 2021-11-23 2022-03-15 上海移为通信技术股份有限公司 无线通信设备及其干扰检测方法
CN114189297B (zh) * 2021-11-23 2024-05-28 上海移为通信技术股份有限公司 无线通信设备及其干扰检测方法
CN114363945A (zh) * 2021-12-22 2022-04-15 苏州迈科网络安全技术股份有限公司 基于网络拓扑的Wifi质量得分计算方法、装置、终端及存储介质
CN114390563A (zh) * 2021-12-30 2022-04-22 中国电信股份有限公司 一种5g小区驻留能力评估方法、装置及电子设备
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CN116614187A (zh) * 2023-07-05 2023-08-18 广州市梦享网络技术有限公司 一种信号强度确定方法、装置、设备及存储介质
CN116614187B (zh) * 2023-07-05 2024-01-26 广州市梦享网络技术有限公司 一种信号强度确定方法、装置、设备及存储介质
CN117412315A (zh) * 2023-12-12 2024-01-16 深圳通诚无限科技有限公司 一种基于数据分析的无线通讯网络数据优化方法
CN117412315B (zh) * 2023-12-12 2024-03-15 深圳通诚无限科技有限公司 一种基于数据分析的无线通讯网络数据优化方法

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