CN109041084B - Wireless network optimization method - Google Patents
Wireless network optimization method Download PDFInfo
- Publication number
- CN109041084B CN109041084B CN201811060613.9A CN201811060613A CN109041084B CN 109041084 B CN109041084 B CN 109041084B CN 201811060613 A CN201811060613 A CN 201811060613A CN 109041084 B CN109041084 B CN 109041084B
- Authority
- CN
- China
- Prior art keywords
- communication base
- base station
- drive test
- information
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention provides a wireless network optimization method, which comprises a road test terminal, a communication base station and a network optimizer, wherein the network optimizer comprises a receiving module, a sending module, a neural learning module and a storage module, the neural learning module is used for learning test information and position information of the position of the road test terminal and obtaining an optimized data set, and the optimized data set is used for obtaining optimal parameters of the communication base station.
Description
Technical Field
The invention relates to the technical field of network communication, in particular to a wireless network optimization method.
Background
The existing wireless network optimization technology generally adopts manual data transmission and transmission parameter adjustment, however, the data transmission amount is explosively increased due to the coming of big data era. The method also makes the current network optimization face a difficult problem, particularly the optimization of the wireless network, and the traditional wireless network optimization needs to count and analyze the drive test result on the test route by means of professional equipment through large-scale drive test, so as to analyze the network problem.
The conventional drive test mode and the optimization method need to use a large amount of manpower and material resources, spend a large amount of time to adjust and regulate the communication base station, and the test mode is not only passive, but also has extremely low efficiency. In particular, for the location of signal handover between adjacent base stations, the determination of the signal source and the handover of the base station are affected by network optimization problems.
Disclosure of Invention
In view of this, the present invention provides a method for optimizing a wireless network, which can improve the working efficiency, reduce the optimization cost, and overcome the problem of signal distribution between base stations.
The technical scheme of the invention is realized as follows: the invention provides a wireless network optimization method, which comprises the following steps:
step one, a drive test terminal uploads position information of a drive test area to a network optimizer, and the network optimizer judges an optimal communication base station of the position according to the position information and sends the optimal communication base station information to the drive test terminal;
and step two, the drive test terminal sends the drive test result of the position to the network optimizer, the network optimizer obtains parameter feedback information according to the drive test result, and the network optimizer adjusts the parameters of the optimal communication base station in the step one according to the parameter feedback information.
On the basis of the above technical solution, preferably, the network optimizer includes a receiving module, a sending module, a neural learning module, and a storage module;
the receiving module is used for receiving data information of the drive test terminal and the communication base station;
the sending module is used for sending data information to the drive test terminal and the communication base station;
the neural learning module is respectively electrically connected with the receiving module, the sending module and the storage module, the neural learning module performs associated learning on the position information and the drive test result received by the receiving module through an artificial neural network and forms an optimized data set of the position information and the drive test result, and the optimized data set is stored in the storage module.
On the basis of the above technical solution, preferably, the working mode of the network optimizer includes: the position information of the drive test terminal and the data information of the drive test result are sent to a receiving module of a network optimizer, the receiving module sends the data information to a neural learning module, the neural learning module is an artificial neural network system, the artificial neural network is used for learning and summarizing the data information and obtaining an optimized data set, the optimized data set is stored in a storage module, when network optimization is needed, the neural learning module calls the stored optimized data set according to the received data information and obtains corresponding parameter feedback information, the parameter feedback information is sent to a communication base station through a sending module, and the communication base station modifies communication parameters.
On the basis of the above technical solution, preferably, each communication base station corresponds to an optimized data set, and the optimized data set is a communication parameter set located at each position within the communication range of the communication base station.
On the basis of the above technical solution, preferably, in the step one, the position information is a coordinate of a position of the drive test terminal in a global coordinate system of a map.
On the basis of the above technical solution, preferably, the method for determining the optimal communication base station is to calculate the distance between the drive test terminal and the adjacent communication base station, and calculate by using the coordinates of the communication base station in the global coordinate system and the coordinates of the drive test terminal, and the communication base station with the shortest distance is the optimal communication base station.
On the basis of the above technical solution, preferably, when the number of the communication base stations that are the shortest from the drive test terminal is not less than two, the drive test terminal simultaneously sends test information to the communication base stations with the same shortest distance, an optimal communication base station is determined according to the feedback information strength of different communication base stations to the same test information, and the communication base station with the strongest feedback information is the optimal communication base station.
On the basis of the above technical solution, preferably, the obtaining manner of the drive test result in the step two includes: the drive test terminal simultaneously adopts a plurality of groups of different transmitting powers and transmitting frequencies to send signals to the optimal communication base station, the optimal communication base station feeds back a plurality of groups of feedback signals with different powers and different frequencies to the signals with different transmitting powers and different transmitting frequencies, each group of transmitting signals with specific transmitting powers and transmitting frequencies is matched with the feedback signals with specific transmitting powers and transmitting frequencies and is arranged into a data packet, and the data packet is a drive test result.
Compared with the prior art, the wireless network optimization method has the following beneficial effects:
(1) according to the technical scheme, the network optimizer is used for processing the road test data and adjusting the parameters of the communication base station, so that the complicated steps of manual adjustment are omitted, then the neural network module with deep learning capability is used for learning the road test data and the adjustment parameters to obtain an optimized data set, the parameters of the communication base station can be directly adjusted to a better value through one road test, the processes of multiple adjustments and road tests in the conventional wireless network optimization process are avoided, the optimized data set of one communication base station or a limited number of communication base stations can be directly applied to wireless network optimization of other communication base stations, the efficiency of wireless network optimization is greatly improved, and the optimization cost is reduced;
(2) the invention also distinguishes and summarizes the communication points between the adjacent base stations, accurately controls the base station distribution of each communication point and avoids the signal loss at the base station switching position.
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of a wireless network optimization method of the present invention;
fig. 2 is a schematic structural diagram of the network optimizer of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, a method for optimizing a wireless network according to the present invention includes:
the road test terminal carries out coordinate positioning on the road test points and sends the positioning coordinates to the network optimizer;
the network optimizer calculates the distance according to the positioning coordinates of the drive test terminal and the coordinates of the communication base station near the drive test terminal, and judges the communication base station with the closest distance;
if there is only one communication base station closest to the drive test terminal, the drive test terminal sends a plurality of groups of test signals with different frequencies and different powers to the communication base station, the communication base station feeds back each signal with a specific frequency and specific power, and the fed back signal also has the specific frequency and power.
In the above, the transmission of multiple sets of test signals at a time is used to confirm which attribute signal should be transmitted by the communication base station at the position corresponding to the point, and therefore both the drive test terminal and the communication base station transmit the multiple sets of signals together.
The drive test terminal correlates the sent signals and the fed-back signals after receiving the feedback signals, the sent signals and the feedback signals with the same frequency and power information are used as a signal group, each signal group and coordinate information at the position are packaged and integrated, then the coordinate information is sent to the network optimizer through the drive test terminal, and the network optimizer utilizes the neural learning module to perform deep learning on the packaged data and forms an optimized data set.
And finally, the network optimizer adjusts the parameters of the communication base station by using an optimized data set comprising position information-drive test information, specifically, the network optimizer learns by using different position information and drive test information, calculates the base station parameters with the lowest sending power and the highest information intensity at each position corresponding to the position information, the parameters are the optimal parameters of the base station, synthesizes the optimal parameters at different positions, summarizes the parameters with the widest practical applicability, and adjusts the communication base station by using the parameters.
If there are a plurality of communication base stations having the same distance from the drive test terminal, the drive test terminal simultaneously transmits signals to the plurality of communication base stations, and the communication base station having the highest intensity of the feedback signal is determined as the communication base station closest to the drive test terminal by comparing the intensities of the same feedback signals of the plurality of communication base stations.
As shown in fig. 2, the work flow of the network optimizer includes: the receiving module receives data information from the drive test terminal and the communication base station and transmits the data information to the neural learning module, the neural learning module associates and deeply learns the position information and the drive test information from the drive test terminal and the parameter feedback information from the communication base station through deep learning to finally obtain an optimized data set, an optimal base station output parameter information is fed back to the input position information and the drive test information through the optimized data set, the information is sent to the drive test terminal and the communication base station, and the communication base station adjusts parameters of the base station through the parameter information.
In a specific embodiment, the network optimizer can also comprehensively utilize the optimized data sets of a plurality of communication base stations for deep learning.
In an embodiment, the nearest communication base station may be used as the origin of the relative coordinate system to calculate the coordinates of the drive test terminal and the distance to the communication base station.
In a specific embodiment, the optimized data sets of the communication base stations with similar terrains are combined and deeply learned again in the neural learning module, and finally a combined optimized data set is obtained, and the combined optimized data set can be directly applied to the communication base stations with similar terrains.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A method for wireless network optimization, the method comprising:
step one, a drive test terminal uploads position information of a drive test area to a network optimizer, and the network optimizer judges an optimal communication base station of the position according to the position information and sends the optimal communication base station information to the drive test terminal;
step two, the drive test terminal sends the drive test result of the position to the network optimizer, the network optimizer obtains a parameter feedback information according to the drive test result, and the network optimizer adjusts the parameter of the optimal communication base station in the step one according to the parameter feedback information;
the network optimizer comprises a receiving module, a sending module, a neural learning module and a storage module, wherein the receiving module is used for receiving data information of a drive test terminal and a communication base station, the sending module is used for sending the data information to the drive test terminal and the communication base station, the neural learning module is respectively electrically connected with the receiving module, the sending module and the storage module, the neural learning module performs associated learning on the position information and the drive test result received by the receiving module through an artificial neural network and forms an optimized data set of the position information and the drive test result, the optimized data set is stored in the storage module, and each communication base station corresponds to one optimized data set;
the working mode of the network optimizer comprises the following steps:
the drive test terminal sends position information of the drive test terminal and data information of a drive test result to a receiving module of a network optimizer, the receiving module sends the data information to a neural learning module, the neural learning module is an artificial neural network system, the artificial neural network is used for learning and summarizing the position information and the drive test result and obtaining an optimized data set, the optimized data set is stored in a storage module, when network optimization is needed, the neural learning module calls the stored optimized data set according to the received data information and obtains corresponding parameter feedback information, the parameter feedback information is sent to a communication base station through a sending module, and the communication base station modifies parameters by using the parameter feedback information.
2. The method as claimed in claim 1, wherein in the first step, the location information is coordinates of a location of the drive test terminal in a global coordinate system of a map.
3. The method of claim 2, wherein in the step one, the method for determining the optimal communication base station is to calculate a distance between the drive test terminal and an adjacent communication base station, and the communication base station with the shortest distance is the optimal communication base station.
4. The method as claimed in claim 3, wherein when the number of the communication base stations with the shortest distance to the drive test terminal is not less than two, the drive test terminal simultaneously sends test information to the communication base stations with the same shortest distance, the optimal communication base station is determined according to the feedback information strength of different communication base stations to the same test information, and the communication base station with the strongest feedback information is the optimal communication base station.
5. The method as claimed in claim 1, wherein the obtaining manner of the drive test result in step two comprises: the drive test terminal simultaneously adopts a plurality of groups of different transmitting powers and frequencies to transmit signals to the optimal communication base station and receive feedback signals from the optimal communication base station, and the transmitting signals with different transmitting powers and frequencies to each group are matched with the feedback signals and are arranged into data packets, wherein the data packets are drive test results.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811060613.9A CN109041084B (en) | 2018-09-11 | 2018-09-11 | Wireless network optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811060613.9A CN109041084B (en) | 2018-09-11 | 2018-09-11 | Wireless network optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109041084A CN109041084A (en) | 2018-12-18 |
| CN109041084B true CN109041084B (en) | 2021-10-26 |
Family
ID=64621589
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811060613.9A Active CN109041084B (en) | 2018-09-11 | 2018-09-11 | Wireless network optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109041084B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6976985B2 (en) * | 2019-04-19 | 2021-12-08 | ソフトバンク株式会社 | How to create an estimation program, how to create a learning data set, an estimation device, an estimation program, an estimation method, and a communication quality improvement system. |
| CN113179527B (en) * | 2020-08-27 | 2023-02-24 | 几维通信技术(深圳)有限公司 | Wireless communication network optimization method and computer-readable storage medium |
| CN113179525B (en) * | 2020-08-27 | 2023-04-07 | 几维通信技术(深圳)有限公司 | Terminal equipment and access network equipment |
| CN115334529A (en) * | 2021-05-10 | 2022-11-11 | 中兴通讯股份有限公司 | Base station performance optimization method, device, base station and storage medium |
| CN115086972B (en) * | 2022-07-19 | 2023-04-07 | 深圳市华曦达科技股份有限公司 | Distributed wireless signal quality optimization method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103533565A (en) * | 2013-11-06 | 2014-01-22 | 北京北方烽火科技有限公司 | Wireless network optimization method and optimizer |
| CN107948919A (en) * | 2017-11-28 | 2018-04-20 | 维沃移动通信有限公司 | A kind of shared method for processing information and mobile terminal |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102202421B (en) * | 2011-04-27 | 2016-05-04 | 宇龙计算机通信科技(深圳)有限公司 | Reminding method and mobile terminal that a kind of signal connects |
| CN111343679A (en) * | 2012-11-02 | 2020-06-26 | 北京三星通信技术研究有限公司 | Method for automatically adjusting mobile parameters |
| US9191869B2 (en) * | 2014-02-27 | 2015-11-17 | Cellco Partnerhip | Optimizing mobile device handoff parameters |
| CN113301620B (en) * | 2014-04-29 | 2022-12-20 | 北京三星通信技术研究有限公司 | Method and equipment for sending switching report and RLF report |
| US11019577B2 (en) * | 2015-10-30 | 2021-05-25 | Telecom Italia S.P.A. | Method and system for dynamically varying reference signals' power in a mobile radio network |
| US10172014B2 (en) * | 2016-12-18 | 2019-01-01 | Dell Products, Lp | Method and apparatus for optimizing selection of radio channel frequency and adaptive clear channel assessment threshold for WLAN access points |
| CN108207005B (en) * | 2016-12-20 | 2020-12-01 | 中国移动通信集团设计院有限公司 | LTE wireless network evaluation method and server |
| CN107437100A (en) * | 2017-08-08 | 2017-12-05 | 重庆邮电大学 | A kind of picture position Forecasting Methodology based on the association study of cross-module state |
| CN107770719A (en) * | 2017-09-27 | 2018-03-06 | 无锡神探电子科技有限公司 | A kind of object localization method based on drive test data and machine learning |
-
2018
- 2018-09-11 CN CN201811060613.9A patent/CN109041084B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103533565A (en) * | 2013-11-06 | 2014-01-22 | 北京北方烽火科技有限公司 | Wireless network optimization method and optimizer |
| CN107948919A (en) * | 2017-11-28 | 2018-04-20 | 维沃移动通信有限公司 | A kind of shared method for processing information and mobile terminal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109041084A (en) | 2018-12-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109041084B (en) | Wireless network optimization method | |
| WO2020164609A1 (en) | Rsrp reporting methods for nr high resolution angle-based downlink positioning | |
| KR101343565B1 (en) | Wireless access point, method and apparatus for controling thereof | |
| EP2919523B1 (en) | Downlink-direction remote radio unit selection decision method and device | |
| PT1757146E (en) | Method for allocating transmission capacities during a signal transmission, base station, and mobile terminal | |
| CN104105185A (en) | Emission power control method, device and system from device to device communication | |
| CN103378950B (en) | A kind of channel state information feedback method and device | |
| JP2014140227A (en) | Transmission/reception control method using margin time, handover method, and communication device | |
| KR20100138723A (en) | Device and method for controlling uplink transmission power in wireless communication system | |
| KR20130087258A (en) | Method and system for providing service in a next generation radio communication system | |
| CN108064075B (en) | Method and apparatus for adjusting reference signal transmit power of one or more cells | |
| CN113596936B (en) | Method, device, equipment and medium for switching wave beams in satellite communication system | |
| CN101998464A (en) | Random access parameter automatic optimization method, system and equipment | |
| CN105392196A (en) | Positioning method and device | |
| CN101854233B (en) | Method and equipment for processing channel quality indication information | |
| CN105792252A (en) | Wireless communication network performance optimization method based on SDN and SDN controller | |
| CN101854658B (en) | Method and system for processing channel quality indication information | |
| US11303334B2 (en) | Communication method and related device | |
| CN110326338A (en) | Method and apparatus for controlling the transmission power in wireless communication system | |
| WO2017190501A1 (en) | Method and system for realizing communication between antenna cloud nodes in indoor high-density network | |
| US20040162100A1 (en) | Radio communication system, radio network controller, mobile station and down link transmission power control method | |
| CN107249213B (en) | A kind of maximized power distribution method of D2D communication Intermediate Frequency spectrum efficiency | |
| CN105704737A (en) | Cell capacity evaluation method and device | |
| GB2621740A (en) | Channel Switching and operating channel validation | |
| CN104023373B (en) | The joint call admission control and Poewr control method of D2D communication systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |