CN103428726B - Antenna angle optimization method and system - Google Patents

Abstract

The invention discloses an antenna angle optimization method and system. Wherein the method includes: collecting a measurement report; parsing the measurement report to obtain sample points, using the sample points of the same antenna as clusters of the antenna; calculating a reasonable direction angle and a reasonable downtilt angle of the antenna according to the cluster cluster of the antenna; Reasonable directional angle and reasonable downtilt angle optimize the directional angle and downtilt angle of the antenna. The present invention uses the measurement reports of the terminal and the base station to calculate the reasonable direction angle and reasonable downtilt angle of the antenna according to the data of the sample points of the same antenna in the measurement report, and uses the obtained reasonable direction angle and reasonable downtilt angle to determine the current direction angle and downtilt angle of the antenna. Optimizing the downtilt angle can quickly, timely, and accurately optimize and adjust the antenna angle, with high efficiency, and does not depend on the work experience of the maintenance personnel. The antenna can be optimized remotely without on-site drive testing.

Description

An antenna angle optimization method and system

technical field

The present invention relates to the field of wireless technology, in particular to an antenna angle optimization method and system.

Background technique

The azimuth and downtilt of the cell antenna are important engineering parameters in the mobile communication network, and the rationality of their setting basically determines the coverage effect of the entire network. With the construction of mobile communication networks and changes in the wireless environment, operators need to optimize and adjust the azimuth and downtilt angles of cell antennas in a timely manner to ensure the overall network coverage and quality requirements.

At present, in the TD-SCDMA network, there are two ways to optimize and adjust the cell antenna:

1. Road test: Carry out road test through DT test software, analyze and find weak coverage, over-area coverage, and unreasonable coverage road sections, then find out the main service area covering this road section, and conduct antenna orientation for this area according to the experience of optimization personnel Optimal adjustment of angle and downtilt;

2. User complaints. According to the long-term complaints of users, the weak coverage area in the network is found. The optimization personnel go to the site to conduct tests, find the cell covering this area, and adjust the antenna azimuth and downtilt angle of the cell according to the optimization experience to meet the location of user complaints. cover.

In the actual production process, the above two optimization methods have the following problems:

1. Optimizing the antenna through drive test is to adjust the direction angle and downtilt angle of the antenna according to the weak coverage or cross-area coverage of individual road sections. Most of the adjusted angles are based on the experience of maintenance personnel, coupled with repeated drive tests and repeated adjustments until the coverage requirements are met. In addition, for some base stations located in high mountains or remote areas with inconvenient traffic, the round-trip time period is long and the efficiency is low; some base stations are located in residential areas or locations that cannot be opened to traffic, and it is not convenient for maintenance personnel to conduct on-site road tests.

2. Optimizing the antenna through user complaints is based on a large number of user complaints reflecting the weak signal in a certain area. After on-site testing by the optimization personnel, the antenna of the nearest cell in the weak coverage area is adjusted to the weak coverage direction. The angle of adjustment is mostly based on the experience of the maintenance personnel. , often lose sight of the other. However, to optimize the antenna through user complaints, the cycle of problem discovery is longer, and the response is made after the user's perception becomes worse, which seriously affects the user's perception.

In addition, it is currently the TD network construction period, and the existing antenna optimization methods cannot guarantee timeliness and accuracy in the face of the optimization of a large number of network-connected base station antennas.

Contents of the invention

In order to solve the technical problems of long antenna angle optimization period and low efficiency in the prior art, the present invention provides an antenna angle optimization method and system.

One aspect of the present invention provides an antenna angle optimization method, including: collecting measurement reports; analyzing the measurement reports to obtain sample points, using the sample points of the same antenna as clusters of the antenna; Calculating a reasonable direction angle and a reasonable downtilt angle of the antenna; optimizing the direction angle and the downtilt angle of the antenna according to the reasonable direction angle and the reasonable downtilt angle.

Another aspect of the present invention provides an antenna angle optimization system, including: a collection device for collecting measurement reports; an analysis device for analyzing measurement reports to obtain sample points, and use the sample points under the same antenna as the antenna The clustering cluster; Angle calculation means, for calculating the reasonable direction angle and reasonable downtilt angle of antenna according to the clustering group of described antenna; Optimizing means, for according to described reasonable direction angle and reasonable downtilt angle to the direction angle of antenna and downtilt angles are optimized.

In the antenna angle optimization method and system of the present invention, by using the measurement reports of the terminal and the base station, the reasonable direction angle and the reasonable downtilt angle of the antenna are calculated according to the data of the sample points of the same antenna in the measurement report, and the obtained reasonable direction angle and the reasonable downtilt angle are used. The tilt angle optimizes the current direction angle and downtilt angle of the antenna, which can quickly, timely and accurately optimize and adjust the antenna angle. The efficiency is high, and it does not depend on the work experience of the maintenance personnel. On-site road test.

Description of drawings

Fig. 1 is a schematic flow chart of an embodiment of the antenna angle optimization method of the present invention;

Fig. 2 is a schematic structural diagram of an embodiment of the antenna angle optimization system of the present invention;

Fig. 3 is a schematic structural view of an embodiment of the angle calculation device of the present invention;

Fig. 4 is a schematic structural diagram of an embodiment of the analysis device of the present invention.

detailed description

The present invention will be described in detail below in conjunction with the accompanying drawings.

The 3GPP standard stipulates that measurement is an important function of the TD-SCDMA system, and the network equipment (NodeB and UE) should have the ability to measure the measurement items specified in the 3GPP standard. Measurement methods include periodic measurement and event-triggered measurement. Measurement items include but are not limited to International Mobile Subscriber Identity (IMSI), measurement sampling time (TIME STAMP), base station number (Scell_ID), received signal code power of Primary Common Control Physical Channel (PCCPCH), Slot interference signal code power, timing advance (Tadv), antenna angle of arrival (AOA), block error rate, UE power level information, etc. (see 3GPP 25.123 Requirements for support of radio resource management (TDD), 3GPP 25.225Physical layer; Measurements (TDD)).

Among them, the value of Tadv in the measurement report ranges from 0 to 1023.875chips, and the specific report values are shown in Table 1:

Table 1

Reported value Measured quantity value unit TIMING_ADVANCE_0000 Timing Advance<0.125 chip TIMING_ADVANCE_0001 0.125<=Timing Advance<0.25 chip ... ... ... TIMING_ADVANCE_1023 127.875<=Timing Advance<128 chip ... ... ... TIMING_ADVANCE_8189 1023.625<=Timing Advance<1023.75 chip TIMING_ADVANCE_8190 1023.75<=Timing Advance<1023.875 chip TIMING_ADVANCE_8191 1023.875<=Timing Advance chip

(From 3GPP specifications TS25.2255.1.14, TS25.123V6.11.09.1.2.2.2).

AOA is a unique measurement item in the TD-SCDMA system. According to the 3GPP specification, AOA takes true north as the reference direction, and the angle (clockwise) between the UE's transmitted signal and true north when it arrives at NodeB. The value of AOA in the measurement report ranges from 0 to 360 degrees, and the specific report values are shown in Table 2:

Table 2

Reported value Measured quantity value unit AOA_ANGLE_000 0<=AOA_ANGLE<0.5 degree AOA_ANGLE_001 0.5<=AOA_ANGLE<1 degree AOA_ANGLE_002 1<=AOA_ANGLE<1.5 degree ... ... ... AOA_ANGLE_717 358.5<=AOA_ANGLE<359 degree AOA_ANGLE_718 359<=AOA_ANGLE<359.5 degree AOA_ANGLE_719 359.5<=AOA_ANGLE<360 degree

(From 3GPP specifications TS25.2255.2.14, TS25.1239.2.1.13.2).

The UE's measurement report is sent to the NodeB through the Uu interface, and the measurement items of the NodeB are uploaded to the RNC through the Iub interface.

The present invention makes full use of the measurement functions of NodeB and UE, and customizes measurement tasks on the Radio Network Controller (RNC) through the Operations&Maintenance Center (OMC) of the equipment manufacturer, and the RNC issues measurement control, requiring NodeB Measure measurement items including Scell_ID, IMSI, TIMESTAMP, Tadv, and AOA with the UE, and report measurement reports periodically.

As shown in FIG. 1 , the steps of the embodiment of the antenna angle optimization method of the present invention will be specifically described below.

Step 102, collecting measurement reports of terminals and base stations;

The measurement report includes the following measurement data: including Scell_ID, UE IMSI, Tadv and AOA.

Step 104, analyze the measurement report, obtain the sample points, and use the sample points of the same antenna as the cluster of the antenna;

Specifically, after the original measurement report data is collected, the report values of the required measurement items are first extracted, and the measurement data are combined in a record-by-item manner, and stored in the format shown in Table 3. Then the values of Tadv and AOA in each record in the measurement report (hereinafter referred to as the report value) are converted into actual measurement values according to the value range of the report value in the 3GPP specification;

According to the relationship between the reported value and the actual measured value in the 3GPP specification (such as Table 1 and Table 2), the present invention converts the reported value into the actual measured value by using a corresponding method in which the reported value is equal to the upper limit of the actual measured value. For example: the reported value of Tadv TIMING_ADVANCE_0000 = 0.125 chips, and the theoretical distance of 1 chip: 300,000,000/1,280,000 = 234.375 meters, so TIMING_ADVANCE_0000 = 29 meters; the reported value of AOA AOA_ANGLE_002 = 1.5 degrees. The analyzed measurement data and storage format are shown in Table 3 below:

table 3

Preferably, smoothing processing is also performed on the obtained data in Table 3 above. Since the measurement cycle is at the millisecond level and there are many sampling points, in order to avoid data differences caused by sampling point jitter and missing reported values, the analyzed measurement data is smoothed and duplicate Tadv and AOA data are deleted. Specifically, the measurement data Tadv and AOA within one second are averaged with the IMSI as the benchmark and the second as the time granularity, so that a large number of repeated Tadv and AOA can be deleted. The sample points that form the clusters with the base station antenna as the unit are shown in Table 4 below:

Table 4

Step 106, calculating a reasonable direction angle and a reasonable downtilt angle of the antenna according to the clustering of the antenna.

Apply the clustering algorithm to iterate the sample points of the cluster clusters output in step 104 with the base station antenna as the unit, and calculate the particle with the smallest cluster quality in the cluster cluster, for example, the kth particle obtained from the calculation is the cluster in the cluster cluster The particle with the smallest mass (α _k , d _k ), where α _k is the angle between the horizontal direction of the main lobe of the antenna and true north, and d _k is the distance between the landing point of the main lobe of the antenna and the base station;

Calculate the reasonable direction angle and reasonable downtilt angle of the antenna according to the particle with the smallest clustering quality, the reasonable direction angle α=α _k of the antenna; calculate the reasonable downtilt angle according to d _k Among them, h is the height of the antenna.

Step 108, optimizing the direction angle and downtilt angle of the antenna according to the reasonable direction angle and the reasonable downtilt angle;

Calculate the difference between the most reasonable azimuth and downtilt (α, θ) of the calculated cell antenna and the actual cell antenna parameters (αo, θo) of the live network, and obtain the difference (αt, θt), that is, the cell The angle at which the antenna needs to be optimally adjusted.

The optimization and adjustment plan of each cell can be formed into a report; the optimization result is associated with the cell Scell-_ID to match, and the optimization plan of the cell antenna can be queried through the cell Scell_ID.

In the above step 106, the clustering algorithm includes: K-MEDOIDS algorithm, K-MEANS algorithm, Clara algorithm and Clarans algorithm, etc. Taking the K-MEDOIDS algorithm and the K-MEANS algorithm as examples, the specific calculation process of the cluster particle (α _k , d _k ) in step 106 will be described below.

1. K-MEDOIDS algorithm

The principle of the K-MEDOIDS algorithm is as follows: first, randomly select a sampling point as the particle of the cluster, and then repeatedly select other sampling points in the cluster to replace the current particle, so as to improve the quality of the cluster. If the replaced sample point becomes a new particle and can reduce the clustering quality of the cluster and make the cluster more compact, then select this sample point as the new particle.

The algorithm uses absolute error to define how compact a cluster is:

$\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}\mathrm{<span\; class="notranslate">\; \&Sigma;p</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; o</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; ,</span>$

Among them, E is the clustering quality, that is, the absolute error, n represents the number of clusters (n=2), p is the iterative sample point, c _n is the cluster to which p belongs, and o _i is the cluster c _n The current mass point, i is the total number of sample points.

If a certain sample point becomes a mass point, the absolute error can be smaller than the absolute error caused by the original mass point, then the K center point algorithm believes that the sample point can replace the original mass point, and when recalculating the cluster mass point in an iteration, we choose The sample point with the smallest cluster quality becomes a new particle.

Example: sample point A–>E1=10

Sample point B–>E2=11

Sample point C–>E3=12

The original particle O–>E4=13, then we elect A as the new particle of the cluster.

If the cluster quality calculated by selecting other sample points is greater than the current particle, we consider the current particle to be the most compact particle of the cluster, and the calculation ends.

2. K-means algorithm

The principle of the K-means algorithm is: first select k objects from n data objects as the initial clustering center; and for the remaining objects, according to their similarity (distance) to these clustering centers, respectively They are assigned to the cluster they are most similar to (represented by the cluster center); the cluster center (the mean of all objects in the cluster) is then calculated for each new cluster obtained; this process is repeated until the standard until the measure function begins to converge.

The algorithm usually uses the mean square error as the standard measure, and the specific formula is as follows:

$\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}\mathrm{<span\; class="notranslate">\; \&Sigma;p</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; ,</span>$

Among them, E is the clustering quality, which represents the sum of the square differences between each object in all clusters and the cluster to which it belongs, K represents the number of cluster objects selected in the cluster, and c _i represents the i-th cluster cluster, P represents the clustering object in the cluster, and mi represents the centroid of the i-th cluster.

If the cluster quality calculated by selecting other sample points is greater than the current particle, we consider the current particle to be the most compact particle of the cluster, and the calculation ends. When the amount of data to be calculated is large, the Clara algorithm or the Clarans algorithm can be used for iterative calculation.

In the method embodiment of the present invention, by using the measurement reports of the terminal and the base station, the reasonable direction angle and the reasonable downtilt angle of the antenna are calculated according to the data of the sample points of the same antenna in the measurement report, and the reasonable direction angle and the reasonable downtilt angle of the antenna are calculated using the obtained reasonable direction angle and reasonable downtilt angle The current bearing and downtilt are optimized. The most reasonable azimuth angle and downtilt angle of the cell antenna calculated based on massive measurement data and scientific mathematical algorithms can quickly, timely and accurately optimize and adjust the antenna angle with high efficiency and does not depend on the work of maintenance personnel Experience, and can take into account both road coverage and depth coverage. The antenna can be optimized remotely without on-site drive test, which fundamentally solves the problem that it is difficult to measure and adjust the antenna for some remote base stations and base stations in residential areas that cannot be tested by drive. It can discover network problems before customers, shorten the problem-solving cycle, and improve user perception. It greatly reduces the TD network construction period, a large number of network base stations, and the optimization workload of cell antennas, shortens the optimization cycle, and ensures network quality.

Based on the same inventive concept, the present invention also provides an antenna angle optimization system. As shown in FIG. 2 , the system embodiment includes: an acquisition device 21 , an analysis device 22 , an angle calculation device 23 , and an optimization device 24 .

Among them, the collection device is used to collect the measurement report of the terminal and the base station; the analysis device is used to analyze the measurement report to obtain the sample point, and the sample point under the same antenna is used as the cluster of the antenna; the angle calculation device is used for The clustering of the antenna calculates the reasonable direction angle and the reasonable downtilt angle of the antenna; the optimization device is used to optimize the direction angle and the downtilt angle of the antenna according to the reasonable direction angle and the reasonable downtilt angle.

Specifically, the angle calculation device uses a clustering algorithm to iterate the sample points in the cluster, and calculates the particle with the smallest clustering quality in the cluster; calculates the reasonable direction angle and reasonable angle of the antenna according to the particle with the smallest clustering quality. Lower inclination.

As shown in FIG. 3 , the angle calculation device includes: a mass point calculation module 31 , a reasonable direction angle calculation module 32 and a reasonable downtilt angle calculation module 33 .

Among them, the mass point calculation module is used to iterate the sample points in the cluster to calculate the clustering quality of the sample points; select the mass point (α _k , d _k ) with the smallest clustering quality, and α _k is the horizontal direction of the main lobe of the antenna The included angle with true north, d _k is the distance between the main lobe of the antenna and the base station; the reasonable direction angle calculation module is used to calculate the reasonable direction angle of the antenna as α _k ; the reasonable downtilt angle calculation module is used to calculate according to d _k The reasonable downtilt angle is Among them, h is the height of the antenna.

The collected measurement report includes the following measurement data: base station number Scell_ID, international mobile subscriber identity IMSI, measurement sampling time, time advance Tadv from terminal to base station, and antenna angle of arrival AOA.

As shown in FIG. 4 , the parsing device includes: a conversion module 41 and a smoothing processing module 42 .

Wherein, the conversion module is used to convert the values of Tadv and AOA in the measurement report into actual measurement values. The smoothing processing module is used for taking the IMSI as a benchmark and calculating average values for Tadv and AOA in a unit time period.

In the system embodiment of the present invention, by using the measurement reports of the terminal and the base station, the reasonable direction angle and the reasonable downtilt angle of the antenna are calculated according to the data of the sample points of the same antenna in the measurement report, and the reasonable direction angle and the reasonable downtilt angle are used for the antenna The current bearing and downtilt are optimized. The most reasonable azimuth angle and downtilt angle of the cell antenna calculated based on massive measurement data and scientific mathematical algorithms can quickly, timely and accurately optimize and adjust the antenna angle with high efficiency and does not depend on the work of maintenance personnel Experience, and can take into account both road coverage and depth coverage. The antenna can be optimized remotely without on-site drive test, which fundamentally solves the problem that it is difficult to measure and adjust the antenna for some remote base stations and base stations in residential areas that cannot be tested by drive. It can discover network problems before customers, shorten the problem-solving cycle, and improve user perception. It greatly reduces the TD network construction period, a large number of network base stations, and the optimization workload of cell antennas, shortens the optimization cycle, and ensures network quality.

It should be noted that: the above embodiments are only used to illustrate the present invention without limitation, and the present invention is not limited to the above-mentioned examples, and all technical solutions and improvements thereof that do not depart from the spirit and scope of the present invention should be included in the present invention. within the scope of the claims.

Claims (8)

1. a kind of antenna angle optimization method, described antenna angle optimization method is applied in td-scdma system, and its feature exists In, comprising:

Collection measurement report；

Parsing measurement report, obtain sample point, using the sample point of the same antenna as described antenna clustering cluster；

Clustering cluster according to described antenna calculates the reasonable direction angle of antenna and reasonable angle of declination；

According to described reasonable direction angle and reasonable angle of declination, the deflection of antenna and angle of declination are optimized；

Clustering cluster according to described antenna calculates the reasonable direction angle of antenna and reasonable angle of declination includes:

Using clustering algorithm, the sample point in described clustering cluster is iterated, calculates clustering result quality in described clustering cluster minimum Particle；

The reasonable direction angle of antenna and reasonable angle of declination are calculated according to the minimum particle of described clustering result quality；

The minimum particle of described clustering result quality is k-th particle, the actual survey of antenna angle of arrival aoa and tadv of k-th particle Value is α_kAnd d_k；

The reasonable direction angle of antenna is calculated according to the minimum particle of described clustering result quality and reasonable angle of declination includes:

Described α_kReasonable direction angle for described antenna；

According to d_kCalculating described reasonable angle of declination isWherein, h hangs height for antenna.

2. antenna angle optimization method according to claim 1 is it is characterised in that described measurement report includes following survey Amount data:

Base station number scell_id, international mobile subscriber identity imsi, measurement sampling time, the time advance of terminal to base station Amount tadv and antenna angle of arrival aoa.

3. antenna angle optimization method according to claim 2 is it is characterised in that parsing measurement report includes:

Numerical value in measurement report for described aoa and tadv is respectively converted into actual measured value α_kAnd d_k, wherein, α_kFor antenna Main lobe horizontal direction and the angle of positive north, d_kFor the distance of antenna main lobe pick-up point and base station, k represents sample point sequence number.

4. antenna angle optimization method according to claim 3 is it is characterised in that parsing measurement report also includes:

On the basis of imsi, tadv and aoa in unit interval is averaged respectively.

5. a kind of aerial angle optimizes system, and described aerial angle optimizes system and is applied in td-scdma system, and its feature exists In, comprising:

Harvester, for gathering measurement report；

Resolver, for parsing measurement report, obtains sample point, and the sample point under the same antenna is poly- as described antenna Class cluster；

Angle calculation device, calculates the reasonable direction angle of antenna and reasonable angle of declination for the clustering cluster according to described antenna；

Optimize device, excellent for being carried out to the deflection of antenna and angle of declination according to described reasonable direction angle and reasonable angle of declination Change；

Described angle calculation device, for being iterated to the sample point in described clustering cluster using clustering algorithm, calculates institute State the minimum particle of clustering result quality in clustering cluster；According to the minimum particle of described clustering result quality calculate antenna reasonable direction angle and Rationally angle of declination；

Described angle calculation device includes:

Particle computing module, for being iterated, calculates the clustering result quality of sample point to the sample point in described clustering cluster；Choose The minimum particle of described clustering result quality is k-th particle, the actual measured value of antenna angle of arrival aoa and tadv of k-th particle For α_kAnd d_k；

Reasonable direction angle computing module, the reasonable direction angle for calculating described antenna is described α_k；

Rationally angle of declination computing module, for according to d_kCalculating described reasonable angle of declination isWherein, h hangs for antenna High.

6. aerial angle according to claim 5 optimizes system it is characterised in that described measurement report includes following survey Amount data:

Base station number scell_id, international mobile subscriber identity imsi, measurement sampling time, the time advance of terminal to base station Amount tadv and antenna angle of arrival aoa.

7. aerial angle according to claim 6 optimizes system it is characterised in that described resolver includes: modulus of conversion Block, is converted to actual measured value α for the numerical value in measurement report by described tadv and aoa_kAnd d_k, wherein, α_kFor antenna master Lobe horizontal direction and the angle of positive north, d_kFor the distance of antenna main lobe pick-up point and base station, k represents sample point sequence number.

8. aerial angle according to claim 7 optimizes system it is characterised in that described resolver also includes: smooth Processing module, for, on the basis of imsi, averaging respectively to tadv and aoa in unit interval.