CN113938908A

CN113938908A - Flow parameter optimization method, device, equipment and storage medium

Info

Publication number: CN113938908A
Application number: CN202010667470.9A
Authority: CN
Inventors: 刘宇; 顾军; 雷婷; 范小丽
Original assignee: ZTE Corp
Current assignee: ZTE Corp
Priority date: 2020-07-13
Filing date: 2020-07-13
Publication date: 2022-01-14
Also published as: WO2022012288A1

Abstract

The invention discloses a method, a device, equipment and a storage medium for optimizing flow parameters, and belongs to the technical field of wireless network communication. The method comprises the following steps: analyzing service distribution conditions of different systems in a layered wireless communication network, and determining a service abnormal site; analyzing the traffic of the abnormal service site, and determining the abnormal service reason of the abnormal service site; and optimizing the flow parameters of the layered wireless communication network according to the abnormal service reasons of the abnormal service sites.

Description

Flow parameter optimization method, device, equipment and storage medium

Technical Field

The present invention relates to the field of wireless network communication technologies, and in particular, to a method, an apparatus, a device, and a storage medium for optimizing a traffic parameter.

Background

With the rapid development of Mobile communication networks and the popularization of intelligent terminals, a conventional 2G Global System for Mobile Communications (GSM)/3G Universal Mobile Telecommunications System (UMTS) network has been transformed to a 4G 3GPP Long Term Evolution (LTE) network and is gradually matured and applied, and continues to evolve towards a 5G New air interface (New Radio, NR) network, and the coexistence of different systems will be a main mode of network networking in a Long period in the future. Therefore, in order to meet the demand for increasingly optimized traffic in the industry, it is necessary to take the hierarchical wireless communication network traffic optimization as a current important research direction. The existing wireless network traffic analysis and promotion method only analyzes from a single system dimension and is difficult to be applied to a layered wireless communication network comprising different systems.

Disclosure of Invention

The embodiments of the present invention mainly aim to provide a method, an apparatus, a device, and a storage medium for optimizing a traffic parameter, which aim to optimize a traffic parameter of a hierarchical wireless communication network.

In order to achieve the above object, an embodiment of the present invention provides a method for optimizing a flow parameter, where the method includes:

analyzing service distribution conditions of different systems in a layered wireless communication network, and determining a service abnormal site;

analyzing the traffic of the abnormal service site, and determining the abnormal service reason of the abnormal service site;

and optimizing the flow parameters of the layered wireless communication network according to the abnormal service reasons of the abnormal service sites.

In order to achieve the above object, an embodiment of the present invention further provides a device for optimizing flow parameters, where the device includes:

the abnormal site determining module is used for analyzing the service distribution conditions of different systems in the layered wireless communication network and determining a service abnormal site;

the abnormal reason determining module is used for carrying out flow analysis on the abnormal service site and determining the abnormal service reason of the abnormal service site;

and the flow optimization module is used for optimizing the flow parameters of the layered wireless communication network according to the service abnormality reasons of the service abnormality sites.

In order to achieve the above object, an embodiment of the present invention further provides a flow parameter optimization device, where the device includes: a memory, a processor, a program stored on the memory and executable on the processor, the program implementing the steps of the above described flow parameter optimization method when executed by the processor.

To achieve the above object, an embodiment of the present invention further provides a storage medium for a computer readable storage, where the storage medium stores one or more programs, and the one or more programs are executable by one or more processors to implement the steps of the above flow parameter optimization method.

The embodiment of the invention provides a method, a device, equipment and a storage medium for optimizing traffic parameters, which determine a traffic abnormal site by analyzing traffic distribution of different systems of a hierarchical wireless communication network, determine a cause of the traffic abnormal, such as uneven network coverage of different systems, weak coverage of a high-standard system in a certain sector and the like, determine a traffic parameter corresponding to the cause of the traffic abnormal for the determined cause of the traffic abnormal, and optimize the determined traffic parameter to improve coverage areas of the different systems of the hierarchical wireless communication network, so that the coverage areas of the different systems in the hierarchical wireless communication network are more reasonable.

Drawings

Fig. 1 is a flowchart of a method for optimizing a traffic parameter according to an embodiment of the present invention.

Fig. 2 is an overall analysis flowchart of the optimization of the flow parameters according to the second embodiment of the present invention.

Figure 3 is a flow chart relating to figure 2 identifying whether the UMTS system of a station is a super-busy system;

figure 4 is a flow diagram related to figure 2 identifying whether the LTE system of a station is a super-busy system;

fig. 5 is a flow diagram of LTE potential traffic identification referred to in fig. 2;

fig. 6 is a diagram comparing coverage differences between an LTE system and a UMTS system according to the second embodiment of the present invention;

fig. 7 is a traffic ratio diagram of weak coverage of each sector under the site α according to the second embodiment of the present invention;

fig. 8 is a sector-level traffic ratio diagram of a terminal (or LTE-capable terminal) supporting an LTE system on a network of a UMTS system according to a third embodiment of the present invention;

fig. 9 is a sector-level traffic ratio analysis diagram of a UMTS system and an LTE system according to a third embodiment of the present invention;

fig. 10 is a coverage analysis diagram of a UMTS system and an LTE system in sector 2 according to a third embodiment of the present invention;

fig. 11 is a coverage comparison diagram of an LTE system and a UMTS system in the third embodiment of the present invention;

fig. 12 is a schematic structural block diagram of a traffic parameter optimization apparatus for a layered wireless communication network according to a fourth embodiment of the present invention.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

In the following description, suffixes such as "module", "part", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no peculiar meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.

The embodiment of the invention is applied to the wireless communication network with different system systems, namely a layered wireless communication network. The stations of the hierarchical wireless communication network, namely the wireless communication base stations, can support systems of different systems. A terminal of the hierarchical wireless communication network may reside in a system adapted to its capabilities and traffic occurs and traffic is generated during the residence of the system. For example, in a layered wireless communication network in which a UTMS system and a LTE system exist, stations support the UTMS system and the LTE system, and terminals accessing to the layered wireless communication network include terminals that reside on the UTMS system and generate traffic and terminals that reside on the LTE system and generate traffic, so that traffic-abnormal stations can be determined by counting traffic occurring in the UTMS system and the LTE system, i.e., traffic distribution of the UTMS system and the LTE system, respectively, and by analyzing traffic conditions of the traffic-abnormal stations, including but not limited to counting traffic generated by a terminal that supports LTE residing in each sector of the UTMS system, counting traffic generated by a terminal that supports UTMS residing in each sector of the UTMS system, counting traffic generated by a terminal that supports LTE in a weak coverage area of the LTE system, and the like, the cause of the service abnormality can be determined, and based on the determined cause of the service abnormality, the flow parameter corresponding to the cause of the service abnormality can be determined, so that the determined flow parameter is optimized. The method for optimizing the flow parameters can be implemented by personnel such as network planning, network optimization, operation and maintenance and the like, and can also be manufactured into a flow parameter optimization program or a tool for the personnel to use.

Example one

Fig. 1 is a flowchart of a method for optimizing a flow parameter according to an embodiment of the present invention, and as shown in fig. 1, the method may include:

step S101: and analyzing the service distribution conditions of different systems in the layered wireless communication network to determine the abnormal service site.

The method comprises the steps of determining the business busy degree of different system supported by a site by respectively counting the business of different system supported by the site, and further determining whether the site is a business abnormal site. Taking a first target site supporting a first system and a second system as an example, if the first system of the first target site is a super-busy system and the second system is a non-super-busy system, the first target site is determined as a service abnormal site, or if the second system of the first target site is a super-idle system, the first target site is determined as a service abnormal site. The system of the first system is lower than the system of the second system.

Taking the first standard system of the first target station as a UTMS system and the second standard system as an LTE system as an example, balanced identification of service distribution of different systems is performed in a layered wireless communication network supporting different systems, so as to realize abnormal identification of service distribution of different systems in the layered wireless communication network.

1. If the UTMS system is a super busy system (denoted as "user ≠ Red") and the LTE system is a non-super busy system (denoted as "Lsite ≠ Red"), it is recorded as a traffic distribution anomaly.

And determining whether the first system is a super-busy system or not according to one of a channel unit Utilization rate (CE), a power Utilization rate (TCP non HS) of a non-High Speed Downlink Packet Access (HSDPA) and an HSDPA User number (HSDPA User). That is, for the UMTS system, it is determined whether the definition standard of super-busy in the UMTS system is satisfied by using one of three indexes, namely, availability of CE, TCP non HS, and HSDPA User, exceeding a corresponding threshold, if so, it is determined that the UTMS system is a super-busy system, and it is recorded as "Usite ≠ Red", otherwise, it is determined that the UTMS system is a non-super-busy system, and it is recorded as "Usite ≠ Red". For example, as shown in fig. 3, after acquiring network management data, firstly, taking a cell as a unit, and counting by week, if the availability of CE of the cell is greater than 70%, or the TCP non HS is greater than 60%, or the HSDPA User is greater than 30 (that is, the cell-level week granularity satisfies the "Red" condition), determining that the UTMS supported by the cell is the super-busy system of the week, or the cell covered by the UTMS is the super-busy cell of the week; then, the cell is taken as a unit to count according to months, if three weeks of the UTMS supported by the cell in the continuous four weeks are determined as super-busy cells, or three weeks of the cell covered by the UTMS are determined as super-busy cells in the continuous four weeks (namely, the cell-level month granularity meets the requirement of using Red), the UTMS supported by the cell is determined as the super-busy system of the month, or the cell covered by the UTMS is determined as the super-busy cell of the month; finally, according to sector statistics, if the ratio of the number of super-busy cells to the total number of cells in the month is greater than 50% in the same sector, it is determined that the UMTS system supported by the site is a super-busy system, or the site in the UMTS system is a super-busy site, otherwise, the UTMS system supported by the site is a non-super-busy system, or the site in the UMTS system is a non-super-busy site.

And determining whether the second system is a super-busy system or not according to the PRB Utilization rate and the radio resource control layer connection User number (RRC connection User). For the LTE system, PRB Utilization and RRC Connect User are used for judging whether the definition standard of super-busy in the LTE system is met, if so, the LTE system is a super-busy system and is marked as Lsite ═ Red, otherwise, the LTE system is a non-super-busy system and is marked as Lsite ≠ Red. For example, as shown in fig. 4, after acquiring network management data, firstly, counting by week in a cell as a unit, and if both the PRB utilization and the number of RRC connected users meet corresponding preset conditions (that is, cell-level week granularity meets a condition of LTE ═ Red), determining that an LTE system supported by the cell is a super-busy system in the week, or that the cell covered by the LTE system is a super-busy cell in the week; then, the cell is taken as a unit to count monthly, if three weeks of the LTE system supported by the cell in the consecutive four weeks are determined as a super-busy system, or three weeks of the cell covered by the LTE system in the consecutive four weeks are determined as a super-busy cell (that is, the cell-level monthly granularity satisfies the Lsite — Red condition), it is determined that the LTE system supported by the station corresponding to the cell is a super-busy system, or the station in the LTE system is a super-busy station, or conversely, it is determined that the LTE system supported by the station corresponding to the cell is a non-super-busy system, or the station in the LTE system is a non-super-busy station.

2. And if the traffic (TLsite) of the LTE system is less than or equal to the threshold traffic (TThd), recording the traffic abnormality of the site.

And determining whether the second system is a super-idle system or not according to the flow of the first target station in the second system. That is, an ultra-idle cell definition is introduced into the LTE network, and if the TLsite is smaller than a certain threshold traffic TThd (TLsite is smaller than or equal to TThd), the station traffic anomaly is recorded.

Step S102: the traffic analysis is performed on the abnormal service site, and the reason for the abnormal service of the abnormal service site is determined, specifically, the following step S1021 and step S1022 (not shown in the figure).

Step S1021: and carrying out flow analysis based on the user terminal capabilities of different systems on the site with the abnormal service, and determining the sector with the abnormal flow in the site with the abnormal service.

Specifically, in each sector of a site with abnormal service, determining the traffic generated by the first system residing in the terminal supporting the second system according to the capacity ratio of the terminal supporting the second system and the traffic of all cells in each sector of the second system, and determining the sector with abnormal traffic in the site with abnormal service according to the traffic generated by the first system residing in the terminal supporting the second system. Taking the first standard system as a UTMS system and the second standard system as an LTE system as an example, the method for identifying the relationship between the user terminal capability and the traffic growth of different systems in a layered wireless communication network includes: the traffic generated by the terminal supporting the capability of the LTE system but residing in the UMTS system, which is generated in the UMTS system, can directly quantify and identify the traffic increment brought by the migration of the UMTS system to the LTE system. In actual operation, terminal capability reporting information carried in an RRC reconfiguration signaling in the UMTS system and residence information of the LTE system need to be analyzed, traffic and a traffic proportion generated by the LTE terminal in the UMTS system are obtained, and a site with a proportion 2 times higher than a whole network average value is defined as an observation site.

Further, the step S1021 may further include: the method comprises the steps of obtaining the flow generated by a first system where a terminal supporting a first system resides and the flow generated by a second system where a terminal supporting a second system resides, and determining a sector with abnormal flow in a site with abnormal service according to the flow generated by the first system where the terminal supporting the first system resides and the flow generated by the second system where the terminal supporting the second system resides.

Step S1022: and carrying out coverage balance analysis on the sectors with abnormal flow, and determining the abnormal service reasons of the abnormal service sites according to the analysis result.

Specifically, coverage balance analysis is performed on sectors with abnormal traffic, whether the traffic proportion covered by each sector by the first system and the second system is balanced or not is determined, and when it is determined that the traffic proportion covered by a certain sector by the first system and the second system is not balanced, the uneven network coverage capability of the first system and the second system is determined as a cause of abnormal service of a site with abnormal service.

Taking the first system as a UTMS system and the second system as an LTE system as an example, coverage equalization identification of different systems is performed in a layered wireless communication network, and when the difference in coverage distance between different layers meets the following formula, it is determined that the coverage distance is unbalanced:

wherein UsicDis is the UMTS sector average coverage distance, and LscDi s is the LTE sector average coverage distance.

In this embodiment, it is further determined whether traffic ratios (or traffic ratios) of traffic generated when the terminal resides in the UTMS system and the LTE system in each sector are balanced, for example, if a difference of the traffic ratios is within a preset difference range, it is determined that the traffic ratios covered by the two systems in each sector are balanced, otherwise, it is determined that the traffic ratios covered by the two systems in each sector are unbalanced.

Further, after the flow ratio of the flow generated when the terminal resides in the first system and the second system in a certain sector is determined to be balanced, whether the second system is in weak coverage in each sector is determined, if the second system is determined to be in weak coverage in a certain sector, the flow ratio of the second system in the weak coverage sector is obtained, and when the flow ratio of the weak coverage sector exceeds a preset flow ratio threshold value, the weak coverage of the sector is determined as a service abnormal reason of a service abnormal site. Still taking the case that the first system is the UTMS system and the second system is the LTE system, the traffic analysis of the weak coverage area (i.e., weak field) of the LTE system is performed in the hierarchical wireless communication network, and in the LTE system, the efficiency of promoting the site-level potential traffic can be identified according to the traffic value of the weak coverage area and the ratio of the traffic value of the weak coverage area to the total traffic, so as to assist the sites in the same system in analyzing and processing the priority.

Further, if it is determined that the neighboring site of the abnormal-service site is covered strongly in the weak coverage sector, the strong coverage of the neighboring site is determined as the abnormal-service reason of the abnormal-service site.

Step S103: and optimizing the flow parameters of the layered wireless communication network according to the abnormal service reasons of the abnormal service sites.

Specifically, when the reason of the abnormal service site is that the network coverage capacities of the first system and the second system are not uniform, the lowest access level of the second system is lowered and/or the reference signal transmitting power of the second system is raised to optimize the flow of the layered wireless communication network.

Further, when the reason for the abnormal service of the abnormal service site is weak coverage of the sector, the reference signal transmitting power of the second system is increased.

Furthermore, when the reason for the abnormal service of the abnormal service site is strong coverage of the adjacent site, the reference signal transmitting power of the corresponding cell of the adjacent site is reduced, or the remote power regulation value of the corresponding cell of the adjacent site is increased.

The embodiment can collect all key information in the layered wireless communication network, such as network management data, Radio Frequency (RF) data, measurement information data and other multidimensional data, and formulate the overall flow of flow parameter optimization by combining with the service flow, so as to optimize the flow parameters in the layered wireless communication network, more accurately position the abnormality of different stations in the layered wireless communication network, and provide help for network planning and optimization engineers to quickly converge the problem.

Example two

Fig. 2 is a flowchart of an overall analysis for optimizing traffic parameters of a layered wireless communication network according to a second embodiment of the present invention, as shown in fig. 2, the overall analysis may include the following steps:

the method comprises the steps that firstly, service distribution conditions of different systems in a layered wireless communication network are analyzed, abnormal service sites are determined, and specifically, for a certain site, if a first system supported by the site is a super-busy system and a second system supported by the site is a non-super-busy system, the site is determined as the abnormal service site; or, if the second system of the site is a super-idle system, determining the site as a service abnormal site.

In order to determine whether a different supported by a station is a super-busy system or a super-idle system, a station supporting a UMTS system and an LTE system is taken as an example and implemented steps may include:

firstly, site-level problem statistics of the UMTS system and the LTE system are performed on each site supporting the UMTS system and the LTE system, and regional site-level "day" granularity traffic distribution is statistically analyzed, as shown in table 1.

Table 1 site level day particle size flow distribution table.

Branch	<75G	[75G,125G)	[125G,250G)	[250G,400G)	>＝400G
						Region 1	146	181	385	191	99
Region 2	154	167	260	121	79
						Region 3	79	114	149	69	27
Region 4	53	83	241	153	144
						……

According to the flow of fig. 3, it is identified whether the UMTS system of the station is a super-busy system by using previously collected network management data, for example, at least one of the deactivation of CE, TCP non HS, and HSDPA User, and according to the flow of fig. 4, it is identified whether the LTE system of the station is a super-busy system by using previously collected network management data, for example, PRB deactivation and RRC Connect User. Then, the distribution of the sites in which the UMTS system is super-busy and the LTE system is not super-busy at the site level of the region is statistically analyzed, as shown in table 2.

Table 2. distribution table of super busy stations of systems with different systems of regional station level month granularity.

In this embodiment, on one hand, a station that is super-busy in the UTMS system and not super-busy in the LTE system may be determined as a station that has an abnormal phenomenon (or referred to as a problem station or a station with abnormal service), and on the other hand, a station that has an average daily traffic of the LTE system that is less than or equal to a preset traffic threshold (for example, 75G) may be determined as a station that has an abnormal phenomenon (or referred to as a problem station or a station with abnormal service). Thus, through the statistical analysis, a problem site list (or referred to as a service abnormal site list) can be obtained.

And secondly, carrying out flow analysis on the abnormal service site, and determining the abnormal service reason of the abnormal service site.

For convenience of explanation, in the following analysis, a site α having a daily average traffic of the LTE system of not more than 75G (i.e., a site α where the LTE system is a super-idle system) is selected from the problem site list obtained in the first step and is used as an analysis target, and detailed description is given.

The problem site alpha supports dual systems of LTE and UMTS, and each system has a frequency band. The nearest neighbor of site α is site β, the LTE system (or LTE network) has 3 bands L1800/L900/L2300, and the UMTS system (or UMTS network) has two bands U2100/U880, as shown in Table 3.

Table 3. systems and frequency bands supported by site α and nearest neighbor site β.

Firstly, carrying out flow load check, specifically, carrying out flow analysis based on user terminal capabilities of different systems on a service abnormal site, and determining a sector with abnormal flow in the service abnormal site.

In this embodiment, in each sector of the statistical site α, the traffic generated when the LTE terminal resides in the UMTS system (or UMTS network) may be determined according to the LTE terminal capability ratio and the traffic of all cells in each sector of the LTE system, and the sector with abnormal traffic in the site α may be determined according to the traffic generated when the LTE terminal resides in the UMTS system (or UMTS network). Furthermore, the traffic generated by the UMTS system (or UMTS network) where the UMTS terminal resides and the traffic generated by the UMTS system (or UMTS network) where the LTE terminal resides can be obtained separately, and the sector with abnormal traffic in the site α can be determined according to the traffic generated by the UMTS system (or UMTS network) where the UMTS terminal resides and the traffic generated by the LTE system (or LTE network) where the LTE terminal resides. That is to say, the embodiment may count the traffic of the station α in the UMTS system and the LTE system and the traffic generated by the LTE terminal residing in the UMTS network, as shown in fig. 5, obtain the signaling reported by the terminal through the measurement report, and obtain the support capabilities of all terminals from the signaling, as shown in table 4.

Table 4. terminal support capability table.

Terminals residing in a UMTS system (or UMTS network) and supporting LTE are labeled, and for convenience of description, the embodiment is labeled as User_SuppLInUmts。

To obtain the "day" granularity traffic of the site α in the UMTS system and the LTE system, for convenience of description, the "day" granularity traffic of the UMTS system and the LTE system is respectively denoted as T in this embodiment_PM-UAnd T_PM-L。

Obtaining the total flow A and User of cell statistics through the measurement report_SuppLInUmtsThe generated traffic B determines the LTE terminal capability ratio X, namely X is B/A, and counts the traffic Y of the LTE terminal in the UMTS system (or UMTS network), namely Y is X T_PM-U。

It should be further noted that the LTE-capable terminal traffic residing in or only in the UMTS system may be defined as LTE system (or LTE network) incremental traffic, that is, traffic increments may be brought about when the terminal is migrated from the UMTS system to the LTE system.

Based on the above statistical analysis, the result of the traffic (or traffic) of the UMTS system and the traffic (or traffic) of the LTE system at the problem site α in each sector is shown in table 5.

Table 5. table of the results of the traffic ratio analysis of the UMTS system to the LTE system for the problem site α.

As can be seen from the above table obtained by traffic load examination, the traffic proportion of the LTE terminal of sector 1 on the UMTS system (or UMTS network) is 21.20% (8% of the whole network average), while the other two sectors are only 3.91% and 9.59%, so that sector 1 is determined as the analysis object.

And secondly, after carrying out traffic load inspection, carrying out coverage balance analysis, specifically, carrying out coverage balance analysis on the sectors with abnormal traffic, and determining the cause of the abnormal service of the site alpha according to the analysis result.

The service distribution of the UMTS system and the LTE system is shown in fig. 6, and the coverage geographical location distribution ratio of this embodiment is shown in table 6:

table 6. traffic distribution ratios at different geographical locations of coverage.

System for controlling a power supply

Sector area

Distance1

Distance2

Distance3

Distance4

Distance5

Distance6

UMTS

1

4.27％

15.47％

25.93％

48.65％

4.91％

0.77％

LTE

1

2.95％

42.38％

25.51％

27.40％

1.68％

0.08％

UMTS

2

33.76％

54.57％

7.26％

2.91％

0.93％

0.57％

LTE

2

29.49％

53.16％

11.00％

6.28％

0.05％

0.02％

UMTS

3

13.47％

45.74％

19.54％

18.30％

1.98％

0.97％

LTE

3

14.06％

41.96％

16.25％

25.24％

2.28％

0.21％

As can be seen from table 6, in the sector 1 direction, UMTS coverage is farther than that of LTE, the traffic (or traffic) proportion of the UMTS system in the Distance4 interval is 48.65%, the traffic (or traffic) proportion of LTE in the Distance4 interval is 27.40%, the coverage difference between the two systems is large, that is, the network coverage capability of the two systems in the sector 1 direction is not uniform, and it can be determined that the sector 1 needs to perform traffic parameter optimization.

Further, the present embodiment may also analyze the weak coverage traffic fraction of the LTE system, which is specifically as follows:

in the art, the RSRP is less than or equal to a preset value, which is defined as weak coverage, and the preset value depends on different operators, and may be-110 dbm or-105 dbm, for example.

By periodically reporting the measurement report, a terminal-level reception level and a corresponding traffic are obtained, so that a two-dimensional relationship between RSRP and traffic can be obtained, and further a traffic occupancy of the LTE system of site α under the condition of weak coverage of each sector is obtained, as shown in fig. 7, the weak coverage traffic (or traffic) occupancy of sector 1 reaches 24.67%, is much higher than the occupancy of other two sectors of the same site, and is much higher than the average value of the whole network by 8%, that is, the LTE system is in weak coverage of sector 1, and sector 1 needs to perform traffic parameter optimization.

Further, in this embodiment, when determining that the sector 1 is weakly covered, neighboring stations of the station α may be analyzed, for example, beyond a distance of about 800 meters from the station α, an over-coverage problem exists in an L900M frequency band cell of the external neighboring station β, and an over-coverage ratio of the cell is 69.4%, so that there is traffic absorption competition with the station α, that is, the neighboring station β is strongly covered, and at this time, the traffic parameter of the neighboring station β needs to be optimized.

It should be further explained that, in the embodiment, in combination with traffic analysis and coverage analysis, the weak coverage traffic distributed in the weak coverage area of the LTE system is used to determine the traffic ratio under the weak coverage condition, which is helpful for identifying the traffic gain potential.

And thirdly, optimizing the flow parameters of the layered wireless communication network according to the abnormal service reason of the site alpha.

Through the analysis, the problem that the LTE system of the site α is super-idle is mainly caused by weak coverage of the site and over-coverage of the neighboring site, and for the problem, a flow parameter capable of eliminating the problem can be found and then optimized. For example, for the problem of weak coverage of LTE in sector 1, reference signal transmission Power rsrp (RS Power) of site α may be acquired, and the acquired RS Power of site α is assumed to be 12dbm, which is then increased to 15.2 dbm. For the problem of strong coverage of the neighboring site β of the site α, the neighboring site cell with the cell number of 32 and the RS Power of the neighboring site β can be obtained, and the RS Power of the neighboring site cell with the cell number of 32 is reduced from 13dbm to 10dbm, so as to solve the problem of strong coverage of the neighboring site. In addition, for the problem of overlapping coverage between the site α and the adjacent site β, the problem can be solved by increasing a remote electrical adjustment value (RET) of a corresponding cell of the adjacent site, and specifically, Radio Frequency (RF) data including antenna hanging height, site longitude and latitude, cell direction angle, and the like, and measurement information including a home cell number can be collected in advance, and then the remote electrical adjustment value can be adjusted based on the RF data and the measurement information. The reasons for the service anomaly and the corresponding parameter optimization strategies are shown in table 7:

and 7, service exception reasons and a corresponding parameter optimization strategy table.

After the optimization processing, the flow of an alpha site UMTS system is increased by 11.86%, the flow of an alpha site LTE system is increased by 11.86%, the flow is increased by 81.55%, and the flow of the whole station is increased by 35.45%; together with the improvement of the UMTS traffic by 14.51%, the improvement of the LTE system traffic by 20.79%, and the improvement of the whole area by 17.86% in the whole first neighborhood zone evaluation range, the effect of improving the hierarchical wireless communication network traffic is obvious, as shown in table 8.

And 8, a flow analysis table before and after flow parameter optimization.

And if the problems of unbalanced coverage, weak LTE coverage and the like do not exist, whether the problems are LTE quality problems and user quantity problems are considered, and targeted optimization processing is carried out based on the problems.

On the basis of the above embodiment, after the abnormal site is determined in the first step, it may be further determined whether an alarm related to the site α exists, if the alarm exists, the alarm problem is correspondingly processed, and if the alarm does not exist, that is, the site α has no transmission alarm and no device performance degradation alarm, indicating that the operation state of the base station is normal, the subsequent second step and third step may be performed.

On the basis of the above embodiment, after the abnormal site is determined in the first step, the surveyor can be notified to go to the site α for field survey to assist in determining the cause of the abnormality of the site α.

In this embodiment, when the LTE system is in weak coverage in the sector 1 and the neighboring site β of the site α is in strong coverage, the traffic parameter for eliminating the cause of the service anomaly can be found and optimized, so as to solve the problem that the LTE system of the hierarchical wireless communication network is in weak coverage in the sector 1 and the neighboring site is in strong coverage in the sector 1.

Example 3

The method comprises the steps of firstly, analyzing service distribution conditions of different systems in a layered wireless communication network, and determining a service abnormal site.

In this embodiment, taking the UMTS system supporting the site γ of the UMTS system and the LTE system as a super-busy system and the LTE system as a non-super-busy system as an example, the site γ is subjected to alarm check, and no transmission alarm and no device performance degradation alarm indicate that the operation state of the base station is normal, and the subsequent steps can be continuously performed.

The problem site gamma has two frequency bands of LTE and UMTS, and one frequency band of LTE; the base station platform has seven antennas, and the frequency band and bandwidth distribution of each sector are shown in table 9.

Table 9 shows the frequency band and bandwidth distribution of each sector of the site γ.

In the embodiment, as shown in fig. 8, the traffic proportion of the LTE terminal on the UMTS network is, as can be seen from the figure, in the sector 2, the traffic proportion of the LTE-capable terminal on the UMTS system (or the UMTS network) reaches 44.23%, which is much higher than 8% of the whole network average, and is obviously abnormal.

Sector-level traffic ratio analysis is performed on the UMTS system and the LTE system according to sectors, as shown in fig. 9, similarly in sector 2, the traffic ratio of the LTE system is only 26.03%, and the average value of the whole network is about 65%, and there is an obvious abnormality.

The sector 2 can be determined as an analysis object.

And secondly, after the flow load is checked, performing coverage balance analysis, specifically, performing coverage balance analysis on the sectors with abnormal flow, and determining the reason of the abnormal service site according to the analysis result.

Analyzing the sector 2, comparing the coverage distances of the UMTS system and the LTE system, it is found that the LTE system mainly covers an area below 700 meters, while the UMTS system mainly covers an area from 1 km to 2 km, i.e. an area above 1 km, the coverage of the UMTS system is about 36.05% higher than that of the LTE system, and the coverage difference is significant, as shown in fig. 10.

As can be seen from fig. 11, the UMTS system covers a greater distance than the UMTS system, where there is exactly one village in the UMTS coverage area that belongs to the traffic primary coverage area.

It can be seen that the network coverage of the UMTS system and the UMTS system for site γ are not uniform.

And thirdly, optimizing the flow parameters of the layered wireless communication network according to the abnormal business reasons of the site gamma.

As can be seen from the above analysis, the problem that the UMTS system is super-busy and the LTE system is not super-busy is mainly caused by the non-uniform network coverage capability of the two-system, and therefore, it is necessary to further improve the network coverage capability of the LTE system, specifically, to find the traffic parameters, such as the minimum access level and RSRP of the LTE system, which can improve the network coverage capability of the LTE system, and then adjust the found traffic parameters, as shown in table 10.

TABLE 10 parameter optimization policy Table.

Optimizing a network	Means of optimisation	Parameter name	Detailed description of the invention
				LTE	Optimizing minimum access level	selQrxLevMin	-120dbm->-124dbm
LTE	Boosting transmit power	RS Power	-15.2->～18.1dbm

After the optimization and adjustment, in the sector 2, the traffic of the LTE system is increased by 38.40%, the traffic of the UMTS system is balanced by-7.5%, the total traffic of the hierarchical wireless communication network is increased by 5.30%, and the effect of increasing the traffic of the hierarchical wireless communication network is good, as shown in table 11.

Table 11 flow analysis table before and after flow parameter optimization.

Payload	UMTS(MB)	LTE(MB)	U+L(MB)
				Before adjustment	115194	44383	159578
After adjustment	106518	61445	167964
				Gain of	-7.50％↓	38.40％↑	5.30％↑

The embodiment of the invention is suitable for a multi-system networking scene, the multi-system can be an existing system, such as a UMTS system and an LTE system, and can also be a new generation system with higher spectrum efficiency, and the multi-system can effectively help network regulation, network optimization, operation and maintenance, service delivery personnel to position access points to optimize flow parameters, and perfect network construction and maintenance.

Example four

The embodiment of the invention also provides a flow parameter optimization device, which comprises:

and the abnormal site determining module is used for analyzing the service distribution conditions of different systems in the layered wireless communication network and determining the abnormal service site.

And the abnormal reason determining module is used for analyzing the flow of the abnormal service site and determining the abnormal service reason of the abnormal service site.

By the traffic optimization method provided by the embodiment, the corresponding traffic parameters can be optimized according to the service abnormality reasons of different systems, so that the traffic optimization of the layered wireless communication network comprising different systems is realized.

The device of this embodiment analyzes the service distribution of different systems of the hierarchical wireless communication network to determine a service abnormal site, analyzes the traffic of the service abnormal site to determine a service abnormal cause, for example, uneven network coverage of different systems, weak coverage of a high-standard system in a certain sector, and the like, finds a traffic parameter capable of eliminating the service abnormal cause for the determined service abnormal cause, and optimizes the found traffic parameter to improve the coverage of different systems of the hierarchical wireless communication network, thereby facilitating the traffic promotion of the hierarchical wireless communication network.

EXAMPLE five

An embodiment of the present invention further provides a device for optimizing a flow parameter, where the device includes: a memory, a processor, a program stored on the memory and executable on the processor, the program implementing the steps of the above described flow parameter optimization method when executed by the processor.

EXAMPLE six

The invention may also provide a storage medium for a computer-readable storage, the storage medium storing one or more programs, the one or more programs being executable by one or more processors to perform the specific steps of the above-described method for traffic parameter optimization for a hierarchical wireless communication network.

One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.

In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.

The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, and are not to be construed as limiting the scope of the invention. Any modifications, equivalents and improvements which may occur to those skilled in the art without departing from the scope and spirit of the present invention are intended to be within the scope of the claims.

Claims

1. A method for optimizing flow parameters, the method comprising:

2. The method of claim 1, wherein analyzing the service distribution of different systems in the hierarchical wireless communication network to determine the service abnormal site comprises:

if a first standard system of a first target site is a super-busy system and a second standard system of the first target site is a non-super-busy system, determining the first target site as a service abnormal site;

or if the second standard system of the first target site is a super-idle system, determining the first target site as a service abnormal site;

the first target station is a station supporting a first system and a second system in a layered wireless communication network, and the system of the first system is lower than that of the second system.

3. The method according to claim 2, wherein the system of the first standard is a universal mobile telecommunications system (UTMS) system, and the system of the second standard is a Long Term Evolution (LTE) system, and the method further comprises:

determining whether the first system is a super busy system according to one of the channel unit utilization rate, the non-High Speed Downlink Packet Access (HSDPA) power utilization rate and the HSDPA user number;

determining whether the second system is a super-busy system or not according to the utilization rate of the physical time-frequency resources and the number of users of the wireless resource layer;

and determining whether the second system is a super-idle system or not according to the flow of the second system.

4. The method according to claim 1, wherein the performing traffic analysis on the abnormal service site and determining the cause of the abnormal service at the abnormal service site comprises:

carrying out flow analysis based on user terminal capabilities of different systems on the abnormal service site, and determining a sector with abnormal flow in the abnormal service site;

and carrying out coverage balance analysis on the sectors with abnormal flow, and determining the abnormal service reasons of the abnormal service sites according to the analysis result.

5. The method of claim 4, wherein the performing traffic analysis based on the user terminal capabilities of different systems on the traffic abnormal site, and determining the sector with abnormal traffic in the traffic abnormal site comprises:

in each sector of the abnormal service site, determining the flow generated by the first standard system residing in the terminal supporting the second standard system according to the capacity proportion of the terminal supporting the second standard system and the flow of all the cells of the second standard system in each sector;

and determining the sector with abnormal flow in the abnormal service site according to the flow generated by the first system where the terminal supporting the second system resides.

6. The method of claim 5, wherein the performing traffic analysis based on the user terminal capabilities of different systems on the traffic abnormal site, and determining the sector with abnormal traffic in the traffic abnormal site further comprises:

acquiring traffic generated by a first standard system where a terminal supporting the first standard system resides and traffic generated by a second standard system where a terminal supporting the second standard system resides;

and determining the sectors with abnormal flow in the abnormal service site according to the flow generated by the first system where the terminal supporting the first system resides and the flow generated by the second system where the terminal supporting the second system resides.

7. The method according to claim 4, wherein the performing coverage balance analysis on the sectors with abnormal traffic, and determining the cause of the abnormal traffic at the site with abnormal traffic according to the analysis result comprises:

and when the traffic occupation ratios covered by the first system and the second system in a certain sector are determined to be unbalanced, the uneven network coverage capability of the first system and the second system is determined as a service abnormal reason of the service abnormal site.

8. The method of claim 7, wherein the optimizing the traffic parameters of the hierarchical wireless communication network according to the traffic anomaly cause of the traffic anomaly site comprises:

and when the reason of the abnormal service site is that the network coverage capacities of the first system and the second system are not uniform, reducing the lowest access level of the second system and/or increasing the reference signal transmitting power of the second system.

9. The method according to claim 7, wherein the performing coverage balance analysis on the sectors with abnormal traffic, and determining the cause of the abnormal traffic at the site with abnormal traffic according to the analysis result further comprises:

after determining that the traffic of the first system and the second system in a certain sector is balanced, determining whether the second system in each sector is weakly covered;

if the second system is determined to be weakly covered in a certain sector, acquiring the traffic proportion of the second system in the weakly covered sector, and determining the weak coverage of the sector as a service abnormality reason of the service abnormality site when the traffic proportion of the weakly covered sector exceeds a preset traffic proportion threshold.

10. The method of claim 9, wherein the optimizing the traffic parameters of the hierarchical wireless communication network according to the traffic anomaly cause of the traffic anomaly site comprises:

and when the reason of the abnormal service site is weak coverage of the sector, improving the reference signal transmitting power of the second system.

11. The method according to claim 9, wherein the performing coverage balance analysis on the sectors with abnormal traffic, and determining the cause of the abnormal traffic at the site with abnormal traffic according to the analysis result further comprises:

and if the adjacent site of the abnormal service site is determined to be covered strongly in the weak coverage sector, determining the strong coverage of the adjacent site as the abnormal service reason of the abnormal service site.

12. The method of claim 11, wherein the optimizing the traffic parameters of the hierarchical wireless communication network according to the traffic anomaly cause of the traffic anomaly site comprises:

and when the reason of the abnormal service site is that the adjacent site is covered strongly, reducing the reference signal transmitting power of the corresponding cell of the adjacent site, or improving the remote power regulation value of the corresponding cell of the adjacent site.

13. A flow parameter optimization device, comprising:

14. A flow parameter optimization device, characterized in that the device comprises: memory, processor, program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the flow parameter optimization method according to any one of claims 1 to 12.

15. A storage medium for computer readable storage, wherein the storage medium stores one or more programs which are executable by one or more processors to implement the steps of the flow parameter optimization method of any one of claims 1 to 12.